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Found 205 papers, 60 papers with code
Highland Puebla Nahuatl Speech Translation Corpus for Endangered Language Documentation
Documentation of endangered languages (ELs) has become increasingly urgent as thousands of languages are on the verge of disappearing by the end of the 21st century.
Self-supervised Representation Learning for Speech Processing
Due to the growing popularity of SSL, and the shared mission of the areas in bringing speech and language technologies to more use cases with better quality and scaling the technologies for under-represented languages, we propose this tutorial to systematically survey the latest SSL techniques, tools, datasets, and performance achievement in speech processing.
Zero-shot Learning for Grapheme to Phoneme Conversion with Language Ensemble
Grapheme-to-Phoneme (G2P) has many applications in NLP and speech fields.
Phone Inventories and Recognition for Every Language
Identifying phone inventories is a crucial component in language documentation and the preservation of endangered languages.
CMU’s IWSLT 2022 Dialect Speech Translation System
We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems.
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
The JHU/KyotoU Speech Translation System for IWSLT 2018
This paper describes the Johns Hopkins University (JHU) and Kyoto University submissions to the Speech Translation evaluation campaign at IWSLT2018.
ESPnet How2 Speech Translation System for IWSLT 2019: Pre-training, Knowledge Distillation, and Going Deeper
In this year, we mainly build our systems based on Transformer architectures in all tasks and focus on the end-to-end speech translation (E2E-ST).
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study
Several high-resource Text to Speech (TTS) systems currently produce natural, well-established human-like speech.
4D ASR: Joint modeling of CTC, Attention, Transducer, and Mask-Predict decoders
The network architecture of end-to-end (E2E) automatic speech recognition (ASR) can be classified into several models, including connectionist temporal classification (CTC), recurrent neural network transducer (RNN-T), attention mechanism, and non-autoregressive mask-predict models.
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks
In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape.
Context-aware Fine-tuning of Self-supervised Speech Models
During the fine-tuning stage, we introduce an auxiliary loss that encourages this context embedding vector to be similar to context vectors of surrounding segments.
UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units
We enhance the model performance by subword prediction in the first-pass decoder, advanced two-pass decoder architecture design and search strategy, and better training regularization.
SpeechLMScore: Evaluating speech generation using speech language model
While human evaluation is the most reliable metric for evaluating speech generation systems, it is generally costly and time-consuming.
EURO: ESPnet Unsupervised ASR Open-source Toolkit
This paper describes the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an end-to-end open-source toolkit for unsupervised automatic speech recognition (UASR).
Streaming Joint Speech Recognition and Disfluency Detection
In this study, we propose Transformer-based encoder-decoder models that jointly solve speech recognition and disfluency detection, which work in a streaming manner.
A unified one-shot prosody and speaker conversion system with self-supervised discrete speech units
To address these issues, we devise a cascaded modular system leveraging self-supervised discrete speech units as language representation.
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation
To solve this problem, we would like to simultaneously generate automatic speech recognition (ASR) and ST predictions such that each source language word is explicitly mapped to a target language word.
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding
Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming.
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR
To be specific, we propose to use unsupervised automatic speech recognition (ASR) as a connector that bridges different modalities used in speech and textual pre-trained models.
Minimum Latency Training of Sequence Transducers for Streaming End-to-End Speech Recognition
In this paper, we propose a new training method to explicitly model and reduce the latency of sequence transducer models.
Multi-blank Transducers for Speech Recognition
This paper proposes a modification to RNN-Transducer (RNN-T) models for automatic speech recognition (ASR).
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder
One crucial factor that makes this integration challenging lies in the vocabulary mismatch; the vocabulary constructed for a pre-trained LM is generally too large for E2E-ASR training and is likely to have a mismatch against a target ASR domain.
InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss
This paper presents InterMPL, a semi-supervised learning method of end-to-end automatic speech recognition (ASR) that performs pseudo-labeling (PL) with intermediate supervision.
Towards Zero-Shot Code-Switched Speech Recognition
In this work, we seek to build effective code-switched (CS) automatic speech recognition systems (ASR) under the zero-shot setting where no transcribed CS speech data is available for training.
Avoid Overthinking in Self-Supervised Models for Speech Recognition
Although popular for classification tasks in vision and language, EE has seen less use for sequence-to-sequence speech recognition (ASR) tasks where outputs from early layers are often degenerate.
Self-Supervised Learning
Sequence-To-Sequence Speech Recognition
+1
Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization
In this work, we propose a novel articulatory representation decomposition algorithm that takes the advantage of guided factor analysis to derive the articulatory-specific factors and factor scores.
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model
This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC).
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models
End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation.
In search of strong embedding extractors for speaker diarisation
First, the evaluation is not straightforward because the features required for better performance differ between speaker verification and diarisation.
Large-scale learning of generalised representations for speaker recognition
We also show that training with proposed large data configurations gives better performance.
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency.
Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks
Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long sequence that specifies a hard aligning relationship between the input and target units.
On Compressing Sequences for Self-Supervised Speech Models
Subsampling while training self-supervised models not only improves the overall performance on downstream tasks under certain frame rates, but also brings significant speed-up in inference.
CTC Alignments Improve Autoregressive Translation
Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment.
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization
This paper focuses on speaker diarization and proposes to conduct the above bi-directional knowledge transfer alternately.
E-Branchformer: Branchformer with Enhanced merging for speech recognition
Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the state-of-the-art for automatic speech recognition (ASR).
ESPnet-ONNX: Bridging a Gap Between Research and Production
In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks.
Deep Speech Synthesis from Articulatory Representations
In the articulatory synthesis task, speech is synthesized from input features containing information about the physical behavior of the human vocal tract.
ASR2K: Speech Recognition for Around 2000 Languages without Audio
We achieve 50% CER and 74% WER on the Wilderness dataset with Crubadan statistics only and improve them to 45% CER and 69% WER when using 10000 raw text utterances.
VQ-T: RNN Transducers using Vector-Quantized Prediction Network States
Beam search, which is the dominant ASR decoding algorithm for end-to-end models, generates tree-structured hypotheses.
When Is TTS Augmentation Through a Pivot Language Useful?
Developing Automatic Speech Recognition (ASR) for low-resource languages is a challenge due to the small amount of transcribed audio data.
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
To showcase such integration, we performed experiments on carefully designed synthetic datasets for noisy-reverberant multi-channel ST and SLU tasks, which can be used as benchmark corpora for future research.
Two-Pass Low Latency End-to-End Spoken Language Understanding
End-to-end (E2E) models are becoming increasingly popular for spoken language understanding (SLU) systems and are beginning to achieve competitive performance to pipeline-based approaches.
Online Continual Learning of End-to-End Speech Recognition Models
Continual Learning, also known as Lifelong Learning, aims to continually learn from new data as it becomes available.
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
Conformer has proven to be effective in many speech processing tasks.
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models
In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model.
Improving Speech Enhancement through Fine-Grained Speech Characteristics
We first identify key acoustic parameters that have been found to correlate well with voice quality (e. g. jitter, shimmer, and spectral flux) and then propose objective functions which are aimed at reducing the difference between clean speech and enhanced speech with respect to these features.
Residual Language Model for End-to-end Speech Recognition
In this paper, we propose a simple external LM fusion method for domain adaptation, which considers the internal LM estimation in its training.
LegoNN: Building Modular Encoder-Decoder Models
We present several experiments to demonstrate the effectiveness of LegoNN models: a trained language generation LegoNN decoder module from German-English (De-En) MT task can be reused with no fine-tuning for the Europarl English ASR and the Romanian-English (Ro-En) MT tasks to match or beat respective baseline models.
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors
Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND.
Self-Supervised Speech Representation Learning: A Review
Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years.
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data.
Ranked #3 on
Named Entity Recognition
on SLUE
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation
Although Transformers have gained success in several speech processing tasks like spoken language understanding (SLU) and speech translation (ST), achieving online processing while keeping competitive performance is still essential for real-world interaction.
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation
The present work examines the assumption that combining non-learnable SF extractors to SSL models is an effective approach to low resource speech tasks.
Better Intermediates Improve CTC Inference
This paper proposes a method for improved CTC inference with searched intermediates and multi-pass conditioning.
End-to-End Multi-speaker ASR with Independent Vector Analysis
We develop an end-to-end system for multi-channel, multi-speaker automatic speech recognition.
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation
This work presents our end-to-end (E2E) automatic speech recognition (ASR) model targetting at robust speech recognition, called Integraded speech Recognition with enhanced speech Input for Self-supervised learning representation (IRIS).
Memory-Efficient Training of RNN-Transducer with Sampled Softmax
RNN-Transducer has been one of promising architectures for end-to-end automatic speech recognition.
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers
In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting.
SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy
Deep learning based singing voice synthesis (SVS) systems have been demonstrated to flexibly generate singing with better qualities, compared to conventional statistical parametric based methods.
Investigating self-supervised learning for speech enhancement and separation
Speech enhancement and separation are two fundamental tasks for robust speech processing.
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities
In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.
HEAR: Holistic Evaluation of Audio Representations
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR
As an example application, we use the extended GTC (GTC-e) for the multi-speaker speech recognition task.
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge
This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones.
Acoustic Event Detection with Classifier Chains
In each iteration, the event's activity is estimated and used to condition the next output based on the probabilistic chain rule to form classifier chains.
Conditional Diffusion Probabilistic Model for Speech Enhancement
Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs.
Joint Speech Recognition and Audio Captioning
A major hurdle in evaluating our proposed approach is the lack of labeled audio datasets with both speech transcriptions and audio captions.
Run-and-back stitch search: novel block synchronous decoding for streaming encoder-decoder ASR
To this end, we propose a novel blockwise synchronous decoding algorithm with a hybrid approach that combines endpoint prediction and endpoint post-determination.
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models
The proposed NAR model significantly surpasses previous NAR systems on the AISHELL-1 benchmark and shows a potential for English tasks.
A Study of Transducer based End-to-End ASR with ESPnet: Architecture, Auxiliary Loss and Decoding Strategies
In this study, we present recent developments of models trained with the RNN-T loss in ESPnet.
Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem
Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols, and convert the paradigm of the speech separation/enhancement related tasks from regression to classification.
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type.
ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks.
Attention-based Multi-hypothesis Fusion for Speech Summarization
We propose a cascade speech summarization model that is robust to ASR errors and that exploits multiple hypotheses generated by ASR to attenuate the effect of ASR errors on the summary.
Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity
We demonstrate the effectiveness of our approach in language family classification, speech recognition, and speech synthesis tasks.
Sequence Transduction with Graph-based Supervision
The recurrent neural network transducer (RNN-T) objective plays a major role in building today's best automatic speech recognition (ASR) systems for production.
TorchAudio: Building Blocks for Audio and Speech Processing
This document describes version 0. 10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain.
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions
The deep learning based time-domain models, e. g. Conv-TasNet, have shown great potential in both single-channel and multi-channel speech enhancement.
ESPnet2-TTS: Extending the Edge of TTS Research
This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit.
S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations
In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting.
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation
Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines.
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition
The Transformer architecture has been well adopted as a dominant architecture in most sequence transduction tasks including automatic speech recognition (ASR), since its attention mechanism excels in capturing long-range dependencies.
Multi-Channel End-to-End Neural Diarization with Distributed Microphones
With simulated and real-recorded datasets, we demonstrated that the proposed method outperformed conventional EEND when a multi-channel input was given while maintaining comparable performance with a single-channel input.
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition
We select several pretrained speech representations and present the experimental results on various open-source and publicly available corpora for E2E-ASR.
Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates
We propose Fast-MD, a fast MD model that generates HI by non-autoregressive (NAR) decoding based on connectionist temporal classification (CTC) outputs followed by an ASR decoder.
Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring
We propose a unified NAR E2E-ST framework called Orthros, which has an NAR decoder and an auxiliary shallow AR decoder on top of the shared encoder.
Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker
Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech.
A Study on Speech Enhancement Based on Diffusion Probabilistic Model
Based on this property, we propose a diffusion probabilistic model-based speech enhancement (DiffuSE) model that aims to recover clean speech signals from noisy signals.
Differentiable Allophone Graphs for Language-Universal Speech Recognition
These phone-based systems with learned allophone graphs can be used by linguists to document new languages, build phone-based lexicons that capture rich pronunciation variations, and re-evaluate the allophone mappings of seen language.
On Prosody Modeling for ASR+TTS based Voice Conversion
In voice conversion (VC), an approach showing promising results in the latest voice conversion challenge (VCC) 2020 is to first use an automatic speech recognition (ASR) model to transcribe the source speech into the underlying linguistic contents; these are then used as input by a text-to-speech (TTS) system to generate the converted speech.
Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models
Non-autoregressive (NAR) modeling has gained more and more attention in speech processing.
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors
This makes it possible to produce diarization results of a large number of speakers for the whole recording even if the number of output speakers for each subsequence is limited.
ESPnet-ST IWSLT 2021 Offline Speech Translation System
This year we made various efforts on training data, architecture, and audio segmentation.
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding
Our splits identify performance gaps up to 10% between end-to-end systems that were within 1% of each other on the original test sets.
Automatic Speech Recognition
Natural Language Understanding
+2
Encoder-Decoder Based Attractors for End-to-End Neural Diarization
Diarization results are then estimated as dot products of the attractors and embeddings.
Layer Pruning on Demand with Intermediate CTC
Deploying an end-to-end automatic speech recognition (ASR) model on mobile/embedded devices is a challenging task, since the device computational power and energy consumption requirements are dynamically changed in practice.
Multi-mode Transformer Transducer with Stochastic Future Context
A Multi-mode ASR model can fulfill various latency requirements during inference -- when a larger latency becomes acceptable, the model can process longer future context to achieve higher accuracy and when a latency budget is not flexible, the model can be less dependent on future context but still achieve reliable accuracy.
Multi-Speaker ASR Combining Non-Autoregressive Conformer CTC and Conditional Speaker Chain
Moreover, by including the data of variable numbers of speakers, our model can even better than the PIT-Conformer AR model with only 1/7 latency, obtaining WERs of 19. 9% and 34. 3% on WSJ0-2mix and WSJ0-3mix sets.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10, 000 hours of high quality labeled audio suitable for supervised training, and 40, 000 hours of total audio suitable for semi-supervised and unsupervised training.
Ranked #1 on
Speech Recognition
on GigaSpeech
Leveraging Pre-trained Language Model for Speech Sentiment Analysis
In this paper, we explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis.
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
To evaluate our proposed method, we conduct the experiments of model adaptation using labeled and unlabeled data.
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection
In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND).
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios
Although end-to-end automatic speech recognition (E2E ASR) has achieved great performance in tasks that have numerous paired data, it is still challenging to make E2E ASR robust against noisy and low-resource conditions.
End-to-end ASR to jointly predict transcriptions and linguistic annotations
We propose a Transformer-based sequence-to-sequence model for automatic speech recognition (ASR) capable of simultaneously transcribing and annotating audio with linguistic information such as phonemic transcripts or part-of-speech (POS) tags.
Self-Guided Curriculum Learning for Neural Machine Translation
In the field of machine learning, the well-trained model is assumed to be able to recover the training labels, i. e. the synthetic labels predicted by the model should be as close to the ground-truth labels as possible.
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings
We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings.
SUPERB: Speech processing Universal PERformance Benchmark
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks
In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks.
EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition
Self-supervised ASR-TTS models suffer in out-of-domain data conditions.
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation
To leverage the full potential of the source language information, we propose backward SeqKD, SeqKD from a target-to-source backward NMT model.
SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition
In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models.
Ranked #1 on
Speech Recognition
on SPGISpeech
INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing
The ConferencingSpeech 2021 challenge is proposed to stimulate research on far-field multi-channel speech enhancement for video conferencing.
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yol\'oxochitl Mixtec
{``}Transcription bottlenecks{''}, created by a shortage of effective human transcribers (i. e., transcriber shortage), are one of the main challenges to endangered language (EL) documentation.
End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend
Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions.
Dual-Path Modeling for Long Recording Speech Separation in Meetings
A transformer-based dual-path system is proposed, which integrates transform layers for global modeling.
Gaussian Kernelized Self-Attention for Long Sequence Data and Its Application to CTC-based Speech Recognition
Self-attention (SA) based models have recently achieved significant performance improvements in hybrid and end-to-end automatic speech recognition (ASR) systems owing to their flexible context modeling capability.
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition
In addition to using the prediction error as a metric for evaluating our localization model, we also establish its potency as a frontend with automatic speech recognition (ASR) as the downstream task.
Intermediate Loss Regularization for CTC-based Speech Recognition
In addition, we propose to combine this intermediate CTC loss with stochastic depth training, and apply this combination to a recently proposed Conformer network.
The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge.
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yoloxóchitl Mixtec
"Transcription bottlenecks", created by a shortage of effective human transcribers are one of the main challenges to endangered language (EL) documentation.
A Review of Speaker Diarization: Recent Advances with Deep Learning
Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify "who spoke when".
Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks
As users increasingly rely on cloud-based computing services, it is important to ensure that uploaded speech data remains private.
Arabic Speech Recognition by End-to-End, Modular Systems and Human
Recent advances in automatic speech recognition (ASR) have achieved accuracy levels comparable to human transcribers, which led researchers to debate if the machine has reached human performance.
Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers
We propose a streaming diarization method based on an end-to-end neural diarization (EEND) model, which handles flexible numbers of speakers and overlapping speech.
Speaker Diarization
Sound
Audio and Speech Processing
End-to-End Speaker Diarization as Post-Processing
Clustering-based diarization methods partition frames into clusters of the number of speakers; thus, they typically cannot handle overlapping speech because each frame is assigned to one speaker.
Toward Streaming ASR with Non-Autoregressive Insertion-based Model
We propose a system to concatenate audio segmentation and non-autoregressive ASR to realize high accuracy and low RTF ASR.
Continuous Speech Separation Using Speaker Inventory for Long Multi-talker Recording
Leveraging additional speaker information to facilitate speech separation has received increasing attention in recent years.
Improving RNN Transducer With Target Speaker Extraction and Neural Uncertainty Estimation
Target-speaker speech recognition aims to recognize target-speaker speech from noisy environments with background noise and interfering speakers.
Speech Enhancement
Speech Extraction
+1
Sound
Audio and Speech Processing
Speech Enhancement Guided by Contextual Articulatory Information
Previous studies have confirmed that by augmenting acoustic features with the place/manner of articulatory features, the speech enhancement (SE) process can be guided to consider the articulatory properties of the input speech when performing enhancement to attain performance improvements.
DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs
Several advances have been made recently towards handling overlapping speech for speaker diarization.
Audio and Speech Processing Sound
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis
Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation.
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization
The advantages of D-ASR over existing methods are threefold: (1) it provides explicit speaker locations, (2) it improves the explainability factor, and (3) it achieves better ASR performance as the process is more streamlined.
Improved Mask-CTC for Non-Autoregressive End-to-End ASR
While Mask-CTC achieves remarkably fast inference speed, its recognition performance falls behind that of conventional autoregressive (AR) systems.
Orthros: Non-autoregressive End-to-end Speech Translation with Dual-decoder
Fast inference speed is an important goal towards real-world deployment of speech translation (ST) systems.
Learning Speaker Embedding from Text-to-Speech
TTS with speaker classification loss improved EER by 0. 28\% and 0. 73\% absolutely from a model using only speaker classification loss in LibriTTS and Voxceleb1 respectively.
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice conversion challenge (VCC) 2020.
Augmentation adversarial training for self-supervised speaker recognition
Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general.
Speaker-Conditional Chain Model for Speech Separation and Extraction
With the predicted speaker information from whole observation, our model is helpful to solve the problem of conventional speech separation and speaker extraction for multi-round long recordings.
Audio and Speech Processing Sound
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
This model additionally has a simple and efficient stop criterion for the end of the transduction, making it able to infer the variable number of output sequences.
Ranked #2 on
Speech Separation
on WSJ0-5mix
Streaming Transformer ASR with Blockwise Synchronous Inference
In this paper, we extend block processing towards an entire streaming E2E ASR system without additional training, by introducing a blockwise synchronous decoding process inspired by a neural transducer into the Transformer decoder.
Online End-to-End Neural Diarization with Speaker-Tracing Buffer
This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND).
Neural Speaker Diarization with Speaker-Wise Chain Rule
Speaker diarization is an essential step for processing multi-speaker audio.
Insertion-Based Modeling for End-to-End Automatic Speech Recognition
One NAT model, mask-predict, has been applied to ASR but the model needs some heuristics or additional component to estimate the length of the output token sequence.
Audio and Speech Processing Sound
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors
End-to-end speaker diarization for an unknown number of speakers is addressed in this paper.
Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict
In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC.
Audio and Speech Processing Sound
DiscreTalk: Text-to-Speech as a Machine Translation Problem
This paper proposes a new end-to-end text-to-speech (E2E-TTS) model based on neural machine translation (NMT).
ESPnet-ST: All-in-One Speech Translation Toolkit
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework.
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings
Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6).
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification
However, the clustering-based approach has a number of problems; i. e., (i) it is not optimized to minimize diarization errors directly, (ii) it cannot handle speaker overlaps correctly, and (iii) it has trouble adapting their speaker embedding models to real audio recordings with speaker overlaps.
Speaker Diarization with Region Proposal Network
Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem.
End-to-End Multi-speaker Speech Recognition with Transformer
Recently, fully recurrent neural network (RNN) based end-to-end models have been proven to be effective for multi-speaker speech recognition in both the single-channel and multi-channel scenarios.
End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection
The proposed method is publicly available.
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation.
Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition
In this paper, we study two different non-autoregressive transformer structure for automatic speech recognition (ASR): A-CMLM and A-FMLM.
Towards Online End-to-end Transformer Automatic Speech Recognition
In this paper, we extend it towards an entire online E2E ASR system by introducing an online decoding process inspired by monotonic chunkwise attention (MoChA) into the Transformer decoder.
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.
A practical two-stage training strategy for multi-stream end-to-end speech recognition
The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion.
Transformer ASR with Contextual Block Processing
In this paper, we propose a new block processing method for the Transformer encoder by introducing a context-aware inheritance mechanism.