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Bidisha Sharma

4 accepted papers

2024

Probability-Aware Word-Confusion-Network-To-Text Alignment Approach for Intent Classification

Esaú Villatoro-Tello, Srikanth R. Madikeri, Bidisha Sharma, Driss Khalil, Shashi Kumar, Iuliia Nigmatulina +2
ICASSP 2024accepted

Spoken Language Understanding (SLU) technologies have greatly improved due to the effective pretraining of speech representations. A common requirement of industry-based solutions is the portability to deploy SLU models in voice-assistant devices. Thus, distilling knowledge from large text-based lan…

Cited by 1Source ↗Scholar ↗
2023

Effectiveness of Text, Acoustic, and Lattice-Based Representations in Spoken Language Understanding Tasks

Esaú Villatoro-Tello, Srikanth R. Madikeri, Juan Zuluaga-Gomez, Bidisha Sharma, Seyyed Saeed Sarfjoo, Iuliia Nigmatulina +3
ICASSP 2023accepted

In this paper, we perform an exhaustive evaluation of different representations to address the intent classification problem in a Spoken Language Understanding (SLU) setup. We benchmark three types of systems to perform the SLU intent detection task: 1) text-based, 2) lattice-based, and a novel 3) m…

Cited by 0Source ↗Scholar ↗
2021

Leveraging Acoustic and Linguistic Embeddings from Pretrained Speech and Language Models for Intent Classification

Bidisha Sharma, Maulik C. Madhavi, Haizhou Li
ICASSP 2021accepted

Intent classification is a task in spoken language understanding. An intent classification system is usually implemented as a pipeline process, with a speech recognition module followed by text processing that classifies the intents. There are also studies of end-to-end system that take acoustic fea…

Cited by 24Source ↗Scholar ↗
2019

Automatic Lyrics-to-audio Alignment on Polyphonic Music Using Singing-adapted Acoustic Models

Bidisha Sharma, Chitralekha Gupta, Haizhou Li, Ye Wang
ICASSP 2019accepted

Lyrics-to-audio alignment is to automatically align the lyrical words with the mixed singing audio (singing voice+musical accompaniment). Such alignment can be achieved with an automatic speech recognition (ASR) system. We propose to adapt the acoustic model of a speech recognizer towards solo singi…

Cited by 0Source ↗Scholar ↗