Topic Identification For Speech Without ASR

Abstract

Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs. However, under resource-limited conditions, the manually transcribed speech required to develop standard ASR systems can be severely limited or unavailable. In this paper, we investigate alternative unsupervised solutions to obtaining tokenizations of speech in terms of a vocabulary of automatically discovered word-like or phoneme-like units, without depending on the supervised training of ASR systems. Moreover, using automatic phoneme-like tokenizations, we demonstrate that a convolutional neural network based framework for learning spoken document representations provides competitive performance compared to a standard bag-of-words representation, as evidenced by comprehensive topic ID evaluations on both single-label and multi-label classification tasks.

Stats

• citations8
• S2 citations—
• github stars0
• HF likes0
• heat score7.16
• arxiv keyliu2017topic

Related papers

• Low-resource Contextual Topic Identification On Speech (2018)2.26
• Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information (2023)4.52
• Towards Unsupervised Speech Recognition Without Pronunciation Models (2024)0.00
• Topic Classification On Spoken Documents Using Deep Acoustic And Linguistic Features (2021)5.84
• Unsupervised Automatic Speech Recognition: A Review (2021)13.50
• Unsupervised Neural And Bayesian Models For Zero-resource Speech Processing (2017)0.00
• Intent Recognition And Unsupervised Slot Identification For Low Resourced Spoken Dialog Systems (2021)2.26
• Towards Unsupervised Automatic Speech Recognition Trained By Unaligned Speech And Text Only (2018)0.00