Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features

TL;DR

This paper introduces an unsupervised method for discovering spoken keywords in untranscribed multilingual speech archives, leveraging deep neural networks and pattern mining to identify topic-related words without transcriptions.

Contribution

It presents a novel two-stage approach combining unsupervised acoustic modeling with pattern mining, enabling keyword discovery in low-resource and multilingual speech data.

Findings

Effective extraction of topic-related words from lecture recordings

Utilizes multilingual deep neural networks for acoustic modeling

Achieves promising results without transcriptions

Abstract

The present study tackles the problem of automatically discovering spoken keywords from untranscribed audio archives without requiring word-by-word speech transcription by automatic speech recognition (ASR) technology. The problem is of practical significance in many applications of speech analytics, including those concerning low-resource languages, and large amount of multilingual and multi-genre data. We propose a two-stage approach, which comprises unsupervised acoustic modeling and decoding, followed by pattern mining in acoustic unit sequences. The whole process starts by deriving and modeling a set of subword-level speech units with untranscribed data. With the unsupervisedly trained acoustic models, a given audio archive is represented by a pseudo transcription, from which spoken keywords can be discovered by string mining algorithms. For unsupervised acoustic modeling, a deep neural network trained by multilingual speech corpora is used to generate speech segmentation and compute bottleneck features for segment clustering. Experimental results show that the proposed system is able to effectively extract topic-related words and phrases from the lecture recordings on MIT OpenCourseWare.