Search Results for author:
Found 45 papers, 11 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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Large-Scale Evaluation of Speech Foundation Models
In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech.
LV-CTC: Non-autoregressive ASR with CTC and latent variable models
In this paper, we propose a new model combining CTC and a latent variable model, which is one of the state-of-the-art models in the neural machine translation research field.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different Languages
We propose a novel Tri-Modal Translation (TMT) model that translates between arbitrary modalities spanning speech, image, and text.
OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer
In this work, we aim to improve the performance and efficiency of OWSM without extra training data.
Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification.
HuBERTopic: Enhancing Semantic Representation of HuBERT through Self-supervision Utilizing Topic Model
In this paper, we propose a new approach to enrich the semantic representation of HuBERT.
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling.
Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing
Recent works in end-to-end speech-to-text translation (ST) have proposed multi-tasking methods with soft parameter sharing which leverage machine translation (MT) data via secondary encoders that map text inputs to an eventual cross-modal representation.
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
We show that further efficiency can be achieved with a vanilla HuBERT Base model, which can maintain 94% of XLS-R's performance with only 3% of the data, 4 GPUs, and limited trials.
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Pre-training speech models on large volumes of data has achieved remarkable success.
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks
We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation.
TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition
The model operates on transcripts and audio token sequences and achieves multiple tasks through masking of inputs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation
In detail, we explore multi-channel separation methods, mask-based beamforming and complex spectral mapping, as well as the best features to use in the ASR back-end model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios
The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute
Our code and training optimizations make SSL feasible with only 8 GPUs, instead of the 32 used in the original work.
A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning
Our system achieves state-of-the-art speaker-level detection accuracy (97. 3%), and a relative WER reduction of 11% for moderate Aphasia patients.
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark
Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks.
AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head
In this work, we propose a multi-modal AI system named AudioGPT, which complements LLMs (i. e., ChatGPT) with 1) foundation models to process complex audio information and solve numerous understanding and generation tasks; and 2) the input/output interface (ASR, TTS) to support spoken dialogue.
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms
In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (VoIP) applications.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
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.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
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.
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).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
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.
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.
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.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models
Non-autoregressive (NAR) modeling has gained more and more attention in speech processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings
Then, we propose a novel method using a sequence-to-sequence model, called hypothesis stitcher.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings
However, the model required prior knowledge of speaker profiles to perform speaker identification, which significantly limited the application of the model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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 #3 on
Speech Separation
on WSJ0-4mix
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 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.
MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition
In this work, we propose a novel neural sequence-to-sequence (seq2seq) architecture, MIMO-Speech, which extends the original seq2seq to deal with multi-channel input and multi-channel output so that it can fully model multi-channel multi-speaker speech separation and recognition.
End-to-End Monaural Multi-speaker ASR System without Pretraining
The experiments demonstrate that the proposed methods can improve the performance of the end-to-end model in separating the overlapping speech and recognizing the separated streams.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training
Although great progresses have been made in automatic speech recognition (ASR), significant performance degradation is still observed when recognizing multi-talker mixed speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Recognizing Multi-talker Speech with Permutation Invariant Training
Our technique is based on permutation invariant training (PIT) for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
