Abstract

Robustness against background noise and reverberation is essential for many real-world speech-based applications. One way to achieve this robustness is to employ a speech enhancement front-end that, independently of the back-end, removes the environmental perturbations from the target speech signal. However, although the enhancement front-end typically increases the speech quality from an intelligibility perspective, it tends to introduce distortions which deteriorate the performance of subsequent processing modules. In this paper, we investigate strategies for jointly training neural models for both speech enhancement and the back-end, which optimize a combined loss function. In this way, the enhancement front-end is guided by the back-end to provide more effective enhancement. Differently from typical state-of-the-art approaches employing on spectral features or neural embeddings, we operate in the time domain, processing raw waveforms in both components. As application scenario we consider intent classification in noisy environments. In particular, the front-end speech enhancement module is based on Wave-U-Net while the intent classifier is implemented as a temporal convolutional network. Exhaustive experiments are reported on versions of the Fluent Speech Commands corpus contaminated with noises from the Microsoft Scalable Noisy Speech Dataset, shedding light and providing insight about the most promising training approaches.

Highlights

  • The use of audio-visual platforms e.g., Microsoft Teams, Google Meet, Zoom, etc., for smart-working, remote collaborations and many other applications has been growing exponentially

  • The strategy proposed in this paper aims to jointly adjusting the parameters of a neural speech enhancement model and a neural model designed for a specific task (e.g., Automatic Speech Recognition (ASR), voice activity detection, or intent classification)

  • In this paper we proposed an end-to-end joint training approaches to robust intent classification in noisy environment

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Summary

Introduction

The use of audio-visual platforms e.g., Microsoft Teams, Google Meet, Zoom, etc., for smart-working, remote collaborations and many other applications has been growing exponentially. In these cases, the speech signal is the predominant tool used for communication, and sharing ideas between people [1]. Many speech applications, like Automatic Speech Recognition (ASR), suffer in the presence of these adverse noisy conditions which deteriorate the speech quality and intelligibility, leading to considerable performance drops [6,7], especially in low level of signal-to-noise ratio (SNR). A possible approach is to train, or adapt the models on the noisy data [8].

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