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Found 31 papers, 10 papers with code
Dolphin: A Large-Scale Automatic Speech Recognition Model for Eastern Languages
This report introduces Dolphin, a large-scale multilingual automatic speech recognition (ASR) model that extends the Whisper architecture to support a wider range of languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Integrating Pause Information with Word Embeddings in Language Models for Alzheimer's Disease Detection from Spontaneous Speech
By leveraging speech analysis as a non-invasive and cost-effective tool for AD detection, our research contributes to early diagnosis and improved management of this debilitating disease.
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning
Speech Emotion Recognition (SER) involves analyzing vocal expressions to determine the emotional state of speakers, where the comprehensive and thorough utilization of audio information is paramount.
Improving Acoustic Scene Classification in Low-Resource Conditions
Acoustic Scene Classification (ASC) identifies an environment based on an audio signal.
Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models
Anomalous Sound Detection (ASD) has gained significant interest through the application of various Artificial Intelligence (AI) technologies in industrial settings.
CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy Concerns
Machine anomalous sound detection (ASD) has emerged as one of the most promising applications in the Industrial Internet of Things (IIoT) due to its unprecedented efficacy in mitigating risks of malfunctions and promoting production efficiency.
Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text
It is worth researching how to utilize low-cost data to improve the performance of Whisper on under-represented languages.
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement
Notably, ASR models trained on GigaSpeech 2 can reduce the word error rate for Thai, Indonesian, and Vietnamese on our challenging and realistic YouTube test set by 25% to 40% compared to Whisper large-v3, with merely 10% model parameters.
Simul-Whisper: Attention-Guided Streaming Whisper with Truncation Detection
As a robust and large-scale multilingual speech recognition model, Whisper has demonstrated impressive results in many low-resource and out-of-distribution scenarios.
SpeechColab Leaderboard: An Open-Source Platform for Automatic Speech Recognition Evaluation
In this paper we introduce the SpeechColab Leaderboard, a general-purpose, open-source platform designed for ASR evaluation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection
Apart from the knowledge from speech-generic representations, this paper also proposes to simultaneously transfer the knowledge from a speech depression detection task based on the high comorbidity rates of depression and AD.
Task-Agnostic Structured Pruning of Speech Representation Models
Self-supervised pre-trained models such as Wav2vec2, Hubert, and WavLM have been shown to significantly improve many speech tasks.
DistilXLSR: A Light Weight Cross-Lingual Speech Representation Model
Multilingual self-supervised speech representation models have greatly enhanced the speech recognition performance for low-resource languages, and the compression of these huge models has also become a crucial prerequisite for their industrial application.
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning
However, the final model often performs worse on the MT task than the MT model trained alone, which means that the knowledge transfer ability of this method is also limited.
Unsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based Approach
Automatic detection of machine anomaly remains challenging for machine learning.
Cross-lingual Alzheimer's Disease detection based on paralinguistic and pre-trained features
We present our submission to the ICASSP-SPGC-2023 ADReSS-M Challenge Task, which aims to investigate which acoustic features can be generalized and transferred across languages for Alzheimer's Disease (AD) prediction.
MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring
However, it is challenging to develop a single-lead-based ECG interpretation model for multiple diseases diagnosis due to the lack of some key disease information.
Expressive Speech-driven Facial Animation with controllable emotions
It is in high demand to generate facial animation with high realism, but it remains a challenging task.
LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification
It utilizes score variation as an indicator to detect adversarial examples, where the score variation is the absolute discrepancy between the ASV scores of an original audio recording and its transformed audio synthesized from its masked complex spectrogram.
Symmetric Saliency-based Adversarial Attack To Speaker Identification
Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge.
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition
Recent years have witnessed great strides in self-supervised learning (SSL) on the speech processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multilingual Zero Resource Speech Recognition Base on Self-Supervise Pre-Trained Acoustic Models
Labeled audio data is insufficient to build satisfying speech recognition systems for most of the languages in the world.
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 THUEE System Description for the IARPA OpenASR21 Challenge
For the Constrained training condition, we construct our basic ASR system based on the standard hybrid architecture.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
BERT-LID: Leveraging BERT to Improve Spoken Language Identification
It has a profound impact on the multilingual interoperability of an intelligent speech system.
Full Attention Bidirectional Deep Learning Structure for Single Channel Speech Enhancement
As the cornerstone of other important technologies, such as speech recognition and speech synthesis, speech enhancement is a critical area in audio signal processing.
AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style
While recent text to speech (TTS) models perform very well in synthesizing reading-style (e. g., audiobook) speech, it is still challenging to synthesize spontaneous-style speech (e. g., podcast or conversation), mainly because of two reasons: 1) the lack of training data for spontaneous speech; 2) the difficulty in modeling the filled pauses (um and uh) and diverse rhythms in spontaneous speech.
DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling
In this paper, we develop DeepRapper, a Transformer-based rap generation system that can model both rhymes and rhythms.
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
THUEE system description for NIST 2019 SRE CTS Challenge
This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge.
SAM-GCNN: A Gated Convolutional Neural Network with Segment-Level Attention Mechanism for Home Activity Monitoring
To tackle this task, we propose a gated convolutional neural network with segment-level attention mechanism (SAM-GCNN).
