MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events

TL;DR

This paper introduces MT2KD, a two-stage multi-task learning framework that creates a versatile encoder capable of handling speech recognition, audio tagging, and speaker verification with high accuracy and efficiency.

Contribution

The paper presents a novel two-stage training approach combining multi-teacher knowledge distillation and supervised fine-tuning to develop a general-purpose speech and audio encoder.

Findings

Significantly outperforms baseline multi-task models.

Achieves competitive results on ASR, AT, and SV tasks.

Uses only 66M parameters for a versatile encoder.

Abstract

With the advances in deep learning, the performance of end-to-end (E2E) single-task models for speech and audio processing has been constantly improving. However, it is still challenging to build a general-purpose model with high performance on multiple tasks, since different speech and audio processing tasks usually require different training data, input features, or model architectures to achieve optimal performance. In this work, MT2KD, a novel two-stage multi-task learning framework is proposed to build a general-purpose speech and audio encoder that jointly performs three fundamental tasks: automatic speech recognition (ASR), audio tagging (AT) and speaker verification (SV). In the first stage, multi-teacher knowledge distillation (KD) is applied to align the feature spaces of three single-task high-performance teacher encoders into a single student encoder using the same unlabelled data. In the second stage, multi-task supervised fine-tuning is carried out by initialising the model from the first stage and training on the separate labelled data of each single task. Experiments demonstrate that the proposed multi-task training pipeline significantly outperforms a baseline model trained with multi-task learning from scratch. The final system achieves good performance on ASR, AT and SV: with less than 4% relative word-error-rate increase on ASR, only 1.9 lower mean averaged precision on AT and 0.23% absolute higher equal error rate on SV compared to the best-performing single-task encoders, using only a 66M total model parameters.