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arXiv · 2308.05218

Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio

Yang Zhang·Krishna C. Puvvada·Vitaly Lavrukhin·Boris Ginsburg
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Abstract

We propose CONF-TSASR, a non-autoregressive end-to-end time-frequency domain architecture for single-channel target-speaker automatic speech recognition (TS-ASR). The model consists of a TitaNet based speaker embedding module, a Conformer based masking as well as ASR modules. These modules are jointly optimized to transcribe a target-speaker, while ignoring speech from other speakers. For training we use Connectionist Temporal Classification (CTC) loss and introduce a scale-invariant spectrogram reconstruction loss to encourage the model better separate the target-speaker's spectrogram from mixture. We obtain state-of-the-art target-speaker word error rate (TS-WER) on WSJ0-2mix-extr (4.2%). Further, we report for the first time TS-WER on WSJ0-3mix-extr (12.4%), LibriSpeech2Mix (4.2%) and LibriSpeech3Mix (7.6%) datasets, establishing new benchmarks for TS-ASR. The proposed model will be open-sourced through NVIDIA NeMo toolkit.

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BibTeXRIS

Yang Zhang, Krishna C. Puvvada, Vitaly Lavrukhin, Boris Ginsburg. 2023-08-09. Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio. https://doi.org/10.1109/icassp49357.2023.10095115

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