A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer and Large Scale Synthetic Data
| Autor: | Howard Nathan David, Rohit Prabhavalkar, Alexander H. Gruenstein, Alex Park, Turaj Zakizadeh Shabestary |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Signal processing Artificial neural network Computer science Speech recognition Echo (computing) Signal Computer Science - Sound Synthetic data Speech enhancement Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Word (computer architecture) Electrical Engineering and Systems Science - Audio and Speech Processing Communication channel |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp39728.2021.9413585 |
| Popis: | We consider the problem of recognizing speech utterances spoken to a device which is generating a known sound waveform; for example, recognizing queries issued to a digital assistant which is generating responses to previous user inputs. Previous work has proposed building acoustic echo cancellation (AEC) models for this task that optimize speech enhancement metrics using both neural network as well as signal processing approaches. Since our goal is to recognize the input speech, we consider enhancements which improve word error rates (WERs) when the predicted speech signal is passed to an automatic speech recognition (ASR) model. First, we augment the loss function with a term that produces outputs useful to a pre-trained ASR model and show that this augmented loss function improves WER metrics. Second, we demonstrate that augmenting our training dataset of real world examples with a large synthetic dataset improves performance. Crucially, applying SpecAugment style masks to the reference channel during training aids the model in adapting from synthetic to real domains. In experimental evaluations, we find the proposed approaches improve performance, on average, by 57% over a signal processing baseline and 45% over the neural AEC model without the proposed changes. To appear in ICASSP 2021 |
| Databáze: | OpenAIRE |
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