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Found 44 papers, 10 papers with code
Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for Speech Generation
By leveraging Emilia-Pipe, we construct Emilia, the first multilingual speech generation dataset derived from in-the-wild speech data.
SLIDE: Integrating Speech Language Model with LLM for Spontaneous Spoken Dialogue Generation
Recently, ``textless" speech language models (SLMs) based on speech units have made huge progress in generating naturalistic speech, including non-verbal vocalizations.
Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognition
To make domain adaptation more applicable, we address the problem of zero-shot domain adaptation (ZSDA), where target domain data is unavailable in the target language.
Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation
To facilitate the scale-up of Emilia, we also present Emilia-Pipe, the first open-source preprocessing pipeline designed to efficiently transform raw, in-the-wild speech data into high-quality training data with speech annotations.
The Database and Benchmark for the Source Speaker Tracing Challenge 2024
We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks.
TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition
One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in deteriorating SER performance in practice.
Modality-Collaborative Transformer with Hybrid Feature Reconstruction for Robust Emotion Recognition
As a vital aspect of affective computing, Multimodal Emotion Recognition has been an active research area in the multimedia community.
DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition
One persistent challenge in deep learning based speech emotion recognition (SER) is the unconscious encoding of emotion-irrelevant factors (e. g., speaker or phonetic variability), which limits the generalization of SER in practical use.
Enhancing Spoofing Speech Detection Using Rhythm Information
Current spoofing speech detection systems need more convincing evidence.
Synthetic Speech Detection Based on Temporal Consistency and Distribution of Speaker Features
Based on these analyzes, an SSD method based on temporal consistency and distribution of speaker features is proposed.
The Impact of Silence on Speech Anti-Spoofing
First, the reasons for the impact are explored, including the proportion of silence duration and the content of silence.
Improving Short Utterance Anti-Spoofing with AASIST2
The modified Res2Net blocks can extract multi-scale features and improve the detection performance for speech of different durations, thus improving the short utterance evaluation performance.
One-Class Knowledge Distillation for Spoofing Speech Detection
The detection of spoofing speech generated by unseen algorithms remains an unresolved challenge.
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
Firstly, a generalized CTC loss function is introduced to handle noisy pseudo-labels by accepting alternative tokens in the positions of incorrect tokens.
Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture
However, how to deploy hybrid CTC/attention systems for online speech recognition is still a non-trivial problem.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification
Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker verification tasks.
Speech Corpora Divergence Based Unsupervised Data Selection for ASR
This study proposes a unsupervised target-aware data selection method based on speech corpora divergence (SCD), which can measure the similarity between two speech corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-dimensional frequency dynamic convolution with confident mean teacher for sound event detection
Recently, convolutional neural networks (CNNs) have been widely used in sound event detection (SED).
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion
The overfitting of the model to the training set leads to extreme values of the scores and low correlation of the score distributions, which makes score fusion difficult.
Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge
Code-switching automatic speech recognition becomes one of the most challenging and the most valuable scenarios of automatic speech recognition, due to the code-switching phenomenon between multilingual language and the frequent occurrence of code-switching phenomenon in daily life.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines
In the metric aspect, we design the new conversational DER (CDER) evaluation metric, which calculates the SD accuracy at the utterance level.
SASV Based on Pre-trained ASV System and Integrated Scoring Module
Based on the assumption that there is a correlation between anti-spoofing and speaker verification, a Total-Divide-Total integrated Spoofing-Aware Speaker Verification (SASV) system based on pre-trained automatic speaker verification (ASV) system and integrated scoring module is proposed and submitted to the SASV 2022 Challenge.
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization
For online speaker diarization, samples arrive incrementally, and the overall distribution of the samples is invisible.
Boosting Cross-Domain Speech Recognition with Self-Supervision
The cross-domain performance of automatic speech recognition (ASR) could be severely hampered due to the mismatch between training and testing distributions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Decoupled Federated Learning for ASR with Non-IID Data
Secondly, to reduce the communication and computation costs, we propose decoupled federated learning (DecoupleFL).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy
Recently, audio-visual scene classification (AVSC) has attracted increasing attention from multidisciplinary communities.
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset
As a Mandarin speech dataset designed for dialog scenarios with high quality and rich annotations, MagicData-RAMC enriches the data diversity in the Mandarin speech community and allows extensive research on a series of speech-related tasks, including automatic speech recognition, speaker diarization, topic detection, keyword search, text-to-speech, etc.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition
To further exploit the embeddings from different layers of the ASR encoder, we propose a novel CTA-RNN architecture to capture the emotional salient parts of embeddings in both the channel and temporal directions.
Improving CTC-based speech recognition via knowledge transferring from pre-trained language models
Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2. 0 models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition
Nevertheless, most of the previous SSL methods ignore the influence of the background noise or reverberation, which is crucial to deploying ASR systems in real-world speech applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR
In this work, in order to build a better pre-trained model for low-resource ASR, we propose a pre-training approach called wav2vec-S, where we use task-specific semi-supervised pre-training to refine the self-supervised pre-trained model for the ASR task thus more effectively utilize the capacity of the pre-trained model to generate task-specific representations for ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search
Recently neural architecture search(NAS) has been successfully used in image classification, natural language processing, and automatic speech recognition(ASR) tasks for finding the state-of-the-art(SOTA) architectures than those human-designed architectures.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Beam-Guided TasNet: An Iterative Speech Separation Framework with Multi-Channel Output
The proposed framework facilitates iterative signal refinement with the guide of beamforming and seeks to reach the upper bound of the MVDR-based methods.
Multi-Accent Adaptation based on Gate Mechanism
To jointly train the acoustic model and the accent classifier, we propose the multi-task learning with gate mechanism (MTL-G).
Domain Adaptation Using Class Similarity for Robust Speech Recognition
Then, for each class, probabilities of this class are used to compute a mean vector, which we refer to as mean soft labels.
Power pooling: An adaptive pooling function for weakly labelled sound event detection
While this paper focuses on sound event detection applications, the proposed method can be applied to MIL tasks in other domains.
Exploring the time-domain deep attractor network with two-stream architectures in a reverberant environment
Deep attractor networks (DANs) perform speech separation with discriminative embeddings and speaker attractors.
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture
To support the online recognition, we integrate the state reuse chunk-SAE and the MTA based SAD into online CTC/attention architecture.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Utterance-level Permutation Invariant Training with Latency-controlled BLSTM for Single-channel Multi-talker Speech Separation
The experimental results show that uPIT outperforms cPIT when LC-BLSTM is used during inference.
CN-CELEB: a challenging Chinese speaker recognition dataset
These datasets tend to deliver over optimistic performance and do not meet the request of research on speaker recognition in unconstrained conditions.
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling
This technical report describes the IOA team's submission for TASK1A of DCASE2019 challenge.
Weighted Feature Fusion Based Emotional Recognition for Variable-length Speech using DNN
Emotion recognition plays an increasingly important role in human-computer interaction systems, which is a key technology in multimedia communication.
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge
This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
