On the issues of intra-speaker variability and realism in speech, speaker, and language recognition tasks

Hansen, John H. L.; Bořil, Hynek

doi:10.1016/j.specom.2018.05.004

Cited by 24 publications

(10 citation statements)

References 56 publications

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“…Also the recording environment and conditions may differ resulting in higher classification rates. The problems of speaker, environment, acoustic, and technology based mismatch in speech, speaker, and language recognition have been addressed and discussed in details in [58]. In that study, the authors suggested some solutions to enable the collection of more realistic data.…”

Section: Resultsmentioning

confidence: 99%

A comprehensive study on bilingual and multilingual speech emotion recognition using a two-pass classification scheme

Heracleous

Yoneyama

2019

PLoS ONE

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Emotion recognition plays an important role in human-computer interaction. Previously and currently, many studies focused on speech emotion recognition using several classifiers and feature extraction methods. The majority of such studies, however, address the problem of speech emotion recognition considering emotions solely from the perspective of a single language. In contrast, the current study extends monolingual speech emotion recognition to also cover the case of emotions expressed in several languages that are simultaneously recognized by a complete system. To address this issue, a method, which provides an effective and powerful solution to bilingual speech emotion recognition, is proposed and evaluated. The proposed method is based on a two-pass classification scheme consisting of spoken language identification and speech emotion recognition. In the first pass, the language spoken is identified; in the second pass, emotion recognition is conducted using the emotion models of the language identified. Based on deep learning and the i-vector paradigm, bilingual emotion recognition experiments have been conducted using the state-of-the-art English IEMOCAP (four emotions) and German FAU Aibo (five emotions) corpora. Two classifiers along with i-vector features were used and compared, namely, fully connected deep neural networks (DNN) and convolutional neural networks (CNN). In the case of DNN, 64.0% and 61.14% unweighted average recalls (UARs) were obtained using the IEMOCAP and FAU Aibo corpora, respectively. When using CNN, 62.0% and 59.8% UARs were achieved in the case of the IEMOCAP and FAU Aibo corpora, respectively. These results are very promising, and superior to those obtained in similar studies on multilingual or even monolingual speech emotion recognition. Furthermore, an additional baseline approach for bilingual speech emotion recognition was implemented and evaluated. In the baseline approach, six common emotions were considered, and bilingual emotion models were created, trained on data from the two languages. In this case, 51.2% and 51.5% UARs for six emotions were obtained using DNN and CNN, respectively. The results using the baseline method were reasonable and promising, showing the effectiveness of using i-vectors and deep learning in bilingual speech emotion recognition. On the other hand, the proposed two-pass method based on language identification showed significantly superior performance. Furthermore, the current study was extended to also deal with multilingual speech emotion recognition using corpora collected under similar conditions. Specifically, the English IEMOCAP, the German Emo-DB, and a Japanese corpus were used to recognize four emotions based on the proposed two-pass method. The results obtained were very promising, and the differences in UAR were not statistically significant compared to the monolingual classifiers.

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Section: Resultsmentioning

confidence: 99%

A comprehensive study on bilingual and multilingual speech emotion recognition using a two-pass classification scheme

Heracleous

Yoneyama

2019

PLoS ONE

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“…• The speech data should be recorded in environments with varying backgrounds. It ensures robustness against varying background noises for the models trained with the corpus [70]. • To avoid speaker-dependent bias, for each language, data from a large number of speakers should be collected [70].…”

Section: Characteristics Of Standard Speech Corpora For Lid Researchmentioning

confidence: 99%

An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

Dey

Sahidullah

Saha

2022

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

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Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.

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“…1). This variability is called intra-speaker [23]. The quality of the voice, the rate of speech, the degree of articulation are all factors at the basis of acoustic variations for a given signal.…”

Section: Extraction Of Voice Signals and Creation Of A Database By The Mfcc Algorithmmentioning

confidence: 99%

Performance evaluation and implementations of MFCC, SVM and MLP algorithms in the FPGA board

Khamlich

Fathallah

Atouf

et al. 2021

Int. j. electr. comput. eng. syst. (Online)

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One of the most difficult speech recognition tasks is accurate recognition of human-to-human communication. Advances in deep learning over the last few years have produced major speech improvements in recognition on the representative Switch-board conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This raises two issues - what is human performance, and how far down can we still drive speech recognition error rates? The main objective of this article is the development of a comparative study of the performance of Automatic Speech Recognition (ASR) algorithms using a database made up of a set of signals created by female and male speakers of different ages. We will also develop techniques for the Software and Hardware implementation of these algorithms and test them in an embedded electronic card based on a reconfigurable circuit (Field Programmable Gate Array FPGA). We will present an analysis of the results of classifications for the best Support Vector Machine architectures (SVM) and Artificial Neural Networks of Multi-Layer Perceptron (MLP). Following our analysis, we created NIOSII processors and we tested their operations as well as their characteristics. The characteristics of each processor are specified in this article (cost, size, speed, power consumption and complexity). At the end of this work, we physically implemented the architecture of the Mel Frequency Cepstral Coefficients (MFCC) extraction algorithm as well as the classification algorithm that provided the best results.

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On the issues of intra-speaker variability and realism in speech, speaker, and language recognition tasks

Cited by 24 publications

References 56 publications

A comprehensive study on bilingual and multilingual speech emotion recognition using a two-pass classification scheme

A comprehensive study on bilingual and multilingual speech emotion recognition using a two-pass classification scheme

An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

Performance evaluation and implementations of MFCC, SVM and MLP algorithms in the FPGA board

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