Speech Activity Detection Based on Multilingual Speech Recognition System
This work addresses speech activity detection for multilingual and noisy environments, offering incremental improvements over existing methods.
This paper tackles the problem of Speech Activity Detection (SAD) by leveraging a multilingual Automatic Speech Recognition (ASR) system to improve contextual modeling and generalization, resulting in significantly better performance on out-of-domain datasets, such as outperforming baselines by up to 10.0% absolute in Detection Error Rate metrics.
To better model the contextual information and increase the generalization ability of Speech Activity Detection (SAD) system, this paper leverages a multi-lingual Automatic Speech Recognition (ASR) system to perform SAD. Sequence discriminative training of Acoustic Model (AM) using Lattice-Free Maximum Mutual Information (LF-MMI) loss function, effectively extracts the contextual information of the input acoustic frame. Multi-lingual AM training, causes the robustness to noise and language variabilities. The index of maximum output posterior is considered as a frame-level speech/non-speech decision function. Majority voting and logistic regression are applied to fuse the language-dependent decisions. The multi-lingual ASR is trained on 18 languages of BABEL datasets and the built SAD is evaluated on 3 different languages. On out-of-domain datasets, the proposed SAD model shows significantly better performance with respect to baseline models. On the Ester2 dataset, without using any in-domain data, this model outperforms the WebRTC, phoneme recognizer based VAD (Phn Rec), and Pyannote baselines (respectively by 7.1, 1.7, and 2.7% absolute) in Detection Error Rate (DetER) metrics. Similarly, on the LiveATC dataset, this model outperforms the WebRTC, Phn Rec, and Pyannote baselines (respectively by 6.4, 10.0, and 3.7% absolutely) in DetER metrics.