Weighted fast sequential DTW for multilingual audio Query-by-Example retrieval

Vavrek, Jozef; Viszlay, Peter; Lojka, Martin; Juhár, Jozef

doi:10.1007/s10844-018-0499-2

Cited by 15 publications

(11 citation statements)

References 18 publications

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“…Hou et al [11] employs a phone-based speech recognizer and a weight finite-state transducer (WFST)-based search. Vavrek et al [12] uses multilingual phone-based speech recognition, from supervised and unsupervised acoustic models and sequential dynamic time warping for search.…”

Section: Methods Based On the Word/subword Transcription Of The Querymentioning

confidence: 99%

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

Tejedor

Toledano

López-Otero

et al. 2018

J AUDIO SPEECH MUSIC PROC.

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Query-by-example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given an acoustic (spoken) query containing the term of interest as the input. This paper presents the systems submitted to the ALBAYZIN QbE STD 2016 Evaluation held as a part of the ALBAYZIN 2016 Evaluation Campaign at the IberSPEECH 2016 conference. Special attention was given to the evaluation design so that a thorough post-analysis of the main results could be carried out. Two different Spanish speech databases, which cover different acoustic and language domains, were used in the evaluation: the MAVIR database, which consists of a set of talks from workshops, and the EPIC database, which consists of a set of European Parliament sessions in Spanish. We present the evaluation design, both databases, the evaluation metric, the systems submitted to the evaluation, the results, and a thorough analysis and discussion. Four different research groups participated in the evaluation, and a total of eight template matching-based systems were submitted. We compare the systems submitted to the evaluation and make an in-depth analysis based on some properties of the spoken queries, such as query length, single-word/multi-word queries, and in-language/out-of-language queries.

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Section: Methods Based On the Word/subword Transcription Of The Querymentioning

confidence: 99%

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

Tejedor

Toledano

López-Otero

et al. 2018

J AUDIO SPEECH MUSIC PROC.

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show abstract

“…Vavrek et al. [21] proposed a weighted fast sequential dynamic time warping (WFSDTW) algorithm. The acoustic modelling method of the scheme can be employed to build phonetic units, which could be transmitted over cellular mobile networks and the synthetic phonetic units could be identified by WFSDTW at the receiving end.…”

Section: Related Workmentioning

confidence: 99%

A universal data transfer technique over voice channels of cellular mobile communication networks

Zhang

2020

IET Communications

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A universal technique for transmitting data over voice (DoV) links that can be applied to various cellular networks is proposed. In the scheme, a generic modulation model is established based on waveform symbol mapping, where the data to be transmitted are mapped into finite waveform symbols of a codebook generated offline and demodulated with soft decision results at the receiving end. To make the proposed scheme applicable to various kinds of cellular networks, first sinusoidal signals are selected to synthesise waveform symbols due to their stable transmission characteristics over various voice channels. Then, an analytical method based on Surface Packing is proposed to optimise modulation codebooks. Finally, the target demodulation codebook is obtained through learning from the modulation codebook online. Simulation results show that the proposed scheme performs well over voice channels with different vocoders and has low symbol error rate on various vocoders. Compared with previous schemes designed for some specific vocoders in global system for mobile communication (GSM), the proposed scheme can extend the application scope of DoV to cross‐network scenarios consisting of GSM, universal mobile telecommunications system (UMTS) and long‐term evolution (LTE).

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“…In terms of dimensionality reduction, Vavrek et al [18] proposed a dynamic time warping (DTW) algorithm based on weighted sequence to reduce the dimensionality of audio feature matrix, which improves the performance of precise retrieval and fuzzy retrieval. Cha et al [19] proposed the multiple sub-fingerprint matching principle, offset matching principle and termination strategy, which effectively solved the problem of audio fingerprint dimensionality disaster, and achieved fast and efficient retrieval through high-dimensional audio fingerprint.…”

Section: Introductionmentioning

confidence: 99%

Audio Fingerprint Retrieval Method Based on Feature Dimension Reduction and Feature Combination

2021

KSII TIIS

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In order to solve the problems of the existing audio fingerprint method when extracting audio fingerprints from long speech segments, such as too large fingerprint dimension, poor robustness, and low retrieval accuracy and efficiency, a robust audio fingerprint retrieval method based on feature dimension reduction and feature combination is proposed. Firstly, the Mel-frequency cepstral coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) of the original speech are extracted respectively, and the MFCC feature matrix and LPCC feature matrix are combined. Secondly, the feature dimension reduction method based on information entropy is used for column dimension reduction, and the feature matrix after dimension reduction is used for row dimension reduction based on energy feature dimension reduction method. Finally, the audio fingerprint is constructed by using the feature combination matrix after dimension reduction. When speech's user retrieval, the normalized Hamming distance algorithm is used for matching retrieval. Experiment results show that the proposed method has smaller audio fingerprint dimension and better robustness for long speech segments, and has higher retrieval efficiency while maintaining a higher recall rate and precision rate.

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Weighted fast sequential DTW for multilingual audio Query-by-Example retrieval

Cited by 15 publications

References 18 publications

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

A universal data transfer technique over voice channels of cellular mobile communication networks

Audio Fingerprint Retrieval Method Based on Feature Dimension Reduction and Feature Combination

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