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TASLP Volume 29 | 2021

Audio-Aware Spoken Multiple-Choice Question Answering With Pre-Trained Language Models

Spoken multiple-choice question answering (SMCQA) requires machines to select the correct choice to answer the question by referring to the passage, where the passage, the question, and multiple choices are all in the form of speech. While the audio could contain useful cues for SMCQA, usually only the auto-transcribed text is utilized in model development. Thanks to the large-scaled pre-trained language representation models, such as the bidirectional encoder representations from Transformers (BERT), systems with only auto-transcribed text can still achieve a certain level of performance. 

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Keyword Search Using Attention-Based End-to-End ASR and Frame-Synchronous Phoneme Alignments

Attention-based end-to-end (E2E) automatic speech recognition (ASR) architectures are now the state-of-the-art in terms of recognition performance. However, despite their effectiveness, they have not been widely applied in keyword search (KWS) tasks yet. In this paper, we propose the Att-E2E-KWS architecture, an attention-based E2E ASR framework for KWS that can afford accurate and reliable keyword retrieval results. 

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Modified Magnitude-Phase Spectrum Information for Spoofing Detection

Most of the existing feature representations for spoofing countermeasures consider information either from the magnitude or phase spectrum. We hypothesize that both magnitude and phase spectra can be beneficial for spoofing detection (SD) when collectively used to capture the signal artifacts. In this work, we propose a novel feature referred to as modified magnitude-phase spectrum (MMPS) to capture both magnitude and phase information from the speech signal. 

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Passive Geometry Calibration for Microphone Arrays Based on Distributed Damped Newton Optimization

Geometry calibration is an inherent challenge in distributed acoustic sensor networks. To mitigate this problem, a passive geometry calibration approach based on distributed damped Newton optimization is proposed. Specifically, a geometric cost function incorporating direction of arrivals (DoAs) and time difference of arrivals (TDoAs) is first formulated, and then its identifiability conditions are given.

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Novel Architectures for Unsupervised Information Bottleneck Based Speaker Diarization of Meetings

Speaker diarization is an important problem that is topical, and is especially useful as a preprocessor for conversational speech related applications. The objective of this article is two-fold: (i) segment initialization by uniformly distributing speaker information across the initial segments, and (ii) incorporating speaker discriminative features within the unsupervised diarization framework. In the first part of the work, a varying length segment initialization technique for Information Bottleneck (IB) based speaker diarization system using phoneme rate as the side information is proposed. This initialization distributes speaker information uniformly across the segments and provides a better starting point for IB based clustering. 

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