Resources and Benchmarks for Keyword Search in Spoken Audio From Low-Resource Indian Languages

Nadimpalli, Vijaya Lakshmi V.; Kesiraju, Santosh; Banka, Rohith; Kethireddy, Rashmi; Gangashetty, Suryakanth V.

doi:10.1109/access.2022.3162854

Cited by 3 publications

(1 citation statement)

References 35 publications

(43 reference statements)

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“…There are many languages in India which are referred to as low-resourced (19) languages. Majority of the people around the globe speak a language that is low in resource where scripted data is not available.…”

Section: Impact Of Low-resource Availability On Processing Of Natural...mentioning

confidence: 99%

KHiTE: Multilingual Speech Acquisition to Monolingual Text Translation

Rudrappa¹,

Reddy²,

Hanumanthappa³

2023

IJST

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Objectives: To develop a system that accepts cross-lingual spoken reviews consisting of two to four languages, translate to target language text for Indic languages namely Kannada, Hindi, Telugu and/or English termed as cross lingual speech identification and text translation system. Methods: Hybridization of software engineering models are used in natural languages for pre-processing such as noise removal and speech splitting to obtain phonemes. Combinatorial models namely Hidden-Markov-Model, Artificial Neural Networks, Deep Neural Networks and Convulutional Neural Networks were deployed for direct and indirect speech mapping. Trained corpus consisting of thousand phonemes in the form of wave files for each language considered is named as KHiTEShabdanjali. The basic parameters cosidered for training dataset are pause, pitch, sampling frequency, threshold etc. Findings: The research has resulted in the development of mono-lingual and multi-lingual speech identification, tool for processing of cross-lingual speech and language identification, mono-lingual, bi-lingual, tri-lingual and quad-lingual speech to monolingual text translation for the four languages. It is a generic approach and can be used for other regional languages of India by training the corpus with the selected language. Novelty: Cross-lingual speech identification and text translation system helps users in e-shopping by reducing the time incurred in making a decision to purchase a product having enough features at an economical price, e-tutoring, e-farming activities, digitizing, defence etc.

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Section: Impact Of Low-resource Availability On Processing Of Natural...mentioning

confidence: 99%

KHiTE: Multilingual Speech Acquisition to Monolingual Text Translation

Rudrappa¹,

Reddy²,

Hanumanthappa³

2023

IJST

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Query-by-Example Spoken Term Detection for Zero-Resource Languages Using Heuristic Search

Sudhakar¹,

K²,

Mitra

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Query-by-Example spoken content retrieval is a demanding and challenging task when a large volume of spoken content is piled up in the repositories without annotation. In the absence of annotation, spoken content retrieval is achieved by capturing the similarities between the query and spoken terms from the acoustic feature representation itself. Dynamic Time Warping (DTW) centric techniques identify the optimal alignment between the acoustic feature representations and capture the similarities between query and spoken terms. Despite feasibility, the DTW-centric techniques produce a lot of false alarms due to the variabilities that exist in natural speech and degrade the performance. In the proposed approach, the variability challenges are addressed in two stages. At first, the speaker-independent acoustic feature representation was obtained from the deep convolutional neural networks that reduce the speaker variabilities. In the second stage, the similarities between the query and spoken term were captured using the heuristic search method. The proposed approach was compared with other state-of-the-art methods using Microsoft Low-Resource Language speech corpus. A 3% improvement and 32% reduction in the hit and false alarm ratio were achieved across languages.

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Whisper-based spoken term detection systems for search on speech ALBAYZIN evaluation challenge

Tejedor,

Toledano

2024

J AUDIO SPEECH MUSIC PROC.

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The vast amount of information stored in audio repositories makes necessary the development of efficient and automatic methods to search on audio content. In that direction, search on speech (SoS) has received much attention in the last decades. To motivate the development of automatic systems, ALBAYZIN evaluations include a search on speech challenge since 2012. This challenge releases several databases that cover different acoustic domains (i.e., spontaneous speech from TV shows, conference talks, parliament sessions, to name a few) aiming to build automatic systems that retrieve a set of terms from those databases. This paper presents a baseline system based on the Whisper automatic speech recognizer for the spoken term detection task in the search on speech challenge held in 2022 within the ALBAYZIN evaluations. This baseline system will be released with this publication and will be given to participants in the upcoming SoS ALBAYZIN evaluation in 2024. Additionally, several analyses based on some term properties (i.e., in-language and foreign terms, and single-word and multi-word terms) are carried out to show the Whisper capability at retrieving terms that convey specific properties. Although the results obtained for some databases are far from being perfect (e.g., for broadcast news domain), this Whisper-based approach has obtained the best results on the challenge databases so far so that it presents a strong baseline system for the upcoming challenge, encouraging participants to improve it.

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Resources and Benchmarks for Keyword Search in Spoken Audio From Low-Resource Indian Languages

Cited by 3 publications

References 35 publications

KHiTE: Multilingual Speech Acquisition to Monolingual Text Translation

KHiTE: Multilingual Speech Acquisition to Monolingual Text Translation

Query-by-Example Spoken Term Detection for Zero-Resource Languages Using Heuristic Search

Whisper-based spoken term detection systems for search on speech ALBAYZIN evaluation challenge

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