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Iuliia Nigmatulina

6 accepted papers

2025

Speech Data Selection for Efficient ASR Fine-Tuning using Domain Classifier and Pseudo-Label Filtering

ICASSP 2025accepted

In real-world speech data processing, the scarcity of annotated data and the abundance of unlabelled speech data present a significant challenge. To address this, we propose an efficient data selection pipeline for fine-tuning ASR models by generating pseudo-labels using WhisperX pipeline and select…

Cited by 6SourceScholar
2024

Contextual Biasing Methods for Improving Rare Word Detection in Automatic Speech Recognition

ICASSP 2024accepted

In specialized domains like Air Traffic Control (ATC), a notable challenge in porting a deployed Automatic Speech Recognition (ASR) system from one airport to another is the alteration in the set of crucial words that must be accurately detected in the new environment. Typically, such words have lim…

Cited by 0SourceScholar
2024

Multitask Speech Recognition and Speaker Change Detection for Unknown Number of Speakers

ICASSP 2024accepted

Traditionally, automatic speech recognition (ASR) and speaker change detection (SCD) systems have been independently trained to generate comprehensive transcripts accompanied by speaker turns. Recently, joint training of ASR and SCD systems, by inserting speaker turn tokens in the ASR training text,…

Cited by 0SourceScholar
2024

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

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 1SourceScholar
2023

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

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 0SourceScholar
2022

A Two-Step Approach to Leverage Contextual Data: Speech Recognition in Air-Traffic Communications

ICASSP 2022accepted

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead to a lower number of incidents caused by mis…

Cited by 0SourceScholar