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Found 15 papers, 2 papers with code
Alignment-Free Training for Transducer-based Multi-Talker ASR
Thus, MT-RNNT-AFT can be trained without relying on accurate alignments, and it can recognize all speakers' speech with just one round of encoder processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Boosting Hybrid Autoregressive Transducer-based ASR with Internal Acoustic Model Training and Dual Blank Thresholding
A hybrid autoregressive transducer (HAT) is a variant of neural transducer that models blank and non-blank posterior distributions separately.
NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge
We present a distant automatic speech recognition (DASR) system developed for the CHiME-8 DASR track.
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation
Using these datasets, our study evaluates two types of Transformer-based models: 1) cascade models that combine ASR and strong text summarization models, and 2) end-to-end (E2E) models that directly convert speech into a text summary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Applying LLMs for Rescoring N-best ASR Hypotheses of Casual Conversations: Effects of Domain Adaptation and Context Carry-over
We investigate the effects of domain adaptation of the LLM and context carry-over when performing N-best rescoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
What Do Self-Supervised Speech and Speaker Models Learn? New Findings From a Cross Model Layer-Wise Analysis
Our analysis unveils that 1) the capacity to represent content information is somewhat unrelated to enhanced speaker representation, 2) specific layers of speech SSL models would be partly specialized in capturing linguistic information, and 3) speaker SSL models tend to disregard linguistic information but exhibit more sophisticated speaker representation.
SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?
Self-supervised learning (SSL) for speech representation has been successfully applied in various downstream tasks, such as speech and speaker recognition.
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization
End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model.
Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data
Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture.
Improving Scheduled Sampling for Neural Transducer-based ASR
Experiments in three datasets confirm that RNNT trained with our SS approach achieves the best ASR performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models
However, since the two settings have been studied individually in general, there has been little research focusing on how effective a cross-lingual model is in comparison with a monolingual model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Leveraging Large Text Corpora for End-to-End Speech Summarization
The first technique is to utilize a text-to-speech (TTS) system to generate synthesized speech, which is used for E2E SSum training with the text summary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models
We investigate the performance on SUPERB while varying the structure and KD methods so as to keep the number of parameters constant; this allows us to analyze the contribution of the representation introduced by varying the model architecture.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition
We evaluated this speaker adaptation approach on two low-resource corpora, namely, Ainu and Mboshi.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language
Ainu is an unwritten language that has been spoken by Ainu people who are one of the ethnic groups in Japan.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
