12 accepted papers
This paper explores enhancing streaming speech recognition through the integration of chunk-wise attention and selective state space models (SSMs). The proposed framework replaces the quadratic complexity of attention-based context incorporation with a fully recurrent module based on selective SSMs.…
Extending the RNN Transducer (RNNT) to recognize multi-talker speech is essential for wider automatic speech recognition (ASR) applications. Multi-talker RNNT (MT-RNNT) aims to achieve recognition without relying on costly front-end source separation. MT-RNNT is conventionally implemented using arch…
In recent advancements in end-to-end ASR, large-scale self-supervised or weakly supervised models have achieved a significant milestone. However, it remains challenging to train consistently high-performing multilingual models, transferable to languages without much resource. In this study, we propo…
Time-domain speech enhancement (SE) has recently been intensively investigated. Among recent works, DEMUCS [1] introduces multi-resolution STFT loss to enhance performance. However, some resolutions used for STFT contain non-stationary signals, and it is challenging to learn multi-resolution frequen…
While speech emotion recognition (SER) has been actively studied, the amount and variations of training data are limited compared with speech recognition and speaker recognition tasks. Therefore, it is promising to combine multiple corpora to train a generalized SER model. However, the manner of emo…
The acoustic-to-word (A2W) automatic speech recognition (ASR) realizes very fast decoding with a simple architecture and achieves state-of-the-art performance. However, the A2W model suffers from the out-of-vocabulary (OOV) word problem and cannot use text-only data to improve the language modeling…
This paper addresses end-to-end speech recognition which directly maps acoustic features to a word sequence. The acoustic-to-word model is attractive since it does not require an external language model and an elaborate decoder, resulting in extremely simple and fast decoding. The apparent drawback…
Social signals such as laughter and fillers are often observed in natural conversation, and they play various roles in human-to-human communication. Detecting these events is useful for transcription systems to generate rich transcription and for dialogue systems to behave as we do such as synchroni…
This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Al…
This paper presents unsupervised multichannel speech enhancement for noisy speech recognition. Time-frequency (TF) mask estimation has actively been studied for estimating the steering vectors and spatial covariance matrices of speech and noise used for beamforming. The state-of-the-art approach to…
It is very important to exploit abundant unlabeled speech for improving the acoustic model training in automatic speech recognition (ASR). Semi-supervised training methods incorporate unlabeled data in addition to labeled data to enhance the model training, but it encounters the error-prone label pr…
This paper addresses reverberant speech recognition based on front-end processing using DAE (Deep AutoEncoder) coupled with DNN (Deep Neural Network) acoustic model. DAE can effectively and flexibly learn mapping from corrupted speech to the original clean speech based on the deep learning scheme. W…