Conformer-based Target-Speaker Automatic Speech Recognition for
  Single-Channel Audio
Yang Zhang Krishna C. Puvvada Vitaly Lavrukhin Boris Ginsburg
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.