Self-learning speaker identification for enhanced speech recognition

Herbig, Tobias; Gerl, Franz; Minker, Wolfgang

doi:10.1016/j.csl.2011.11.002

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…Değişen ortamlar, konuşulmayan ifadeler, farklı konuşmacılar, farklı telaffuzlar, lehçeler, aksanlar, kelimeler, anlamlar ve konularla başa çıkabilmek için konuşma ve dil işlemenin her seviyesinde öğrenmeye ihtiyaç duyulmaktadır. Bu alandaki araştırmalar hem yeni modellerin öğrenilmesini hem de bu modellerin önceden var olan bilgi kaynaklarına entegrasyonunu ele almaktadır [85]. Örneğin, bir ASR sisteminin giriş konuşmasında yeni bir özel isimle karşılaşabilir ve özel ismin yazımını bulabilmek için eş zamanlı metni uygun içerikle incelemeye ihtiyaç duyulabilir.…”

Section: Asr'de Kendi Kendine öğRenme (Self-learning In Asr)unclassified

Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu

Oyucu

Sever

Polat

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Figure A shows the application area of the Applications Automatic Speech Recognition (ASR) system. Adaptation is required for adaptation processes to be carried out according to the Application area. During the Speech Processing phase, which is the first entry point of the ASR system, feature extraction is performed from the audio signal. Individual properties are obtained by different feature extraction techniques. For example, Mel Frequency Cepstral Coefficient (MFCC) is a feature extraction technique commonly used in speech recognition systems. Decoder, one of the other components of ASR, converts the feature vectors obtained by using Acoustic Model (AM) and Language Model (LM) into phoneme sequences. In acoustic modeling, firstly, the posterior probability of the phoneme within a given time signal is calculated. In the artificial neural network-based acoustic model, the posterior probability of phonemes is independent for each window. This independence means that the phonemes in a word are independent of each other. Figure A. Basic architecture of speech recognition system.Purpose: This study presents a literature review on speech recognition and then discusses the recorded signs of progress made in this area of research for different languages. The data sets used in speech recognition systems, feature extraction approaches, speech recognition methods and performance evaluation criteria are examined and the focus is on the development of speech recognition and the difficulties in this field. Theory and Methods:In this study, literature review (systematic), which is an important component for a scientific article, was carried out. This process was carried out by the combination of different methods. A combination of review approaches is given.Results: According to the information obtained as a result of the research; Computational architectures that can be applied to resistance to the acoustic environment, self-learning in ASR, detection of unknown words, the success of the Turkish ASR at a broad and limited repertoire level, insufficient source status and Automatic Speech Recognition ASR were evaluated. In addition, the future of Turkish ASR was discussed and recommendations were made to overcome the current difficulties for Turkish ASR. Conclusion:The aim of this study is to examine the current speech recognition methods and approaches and to present the developments in this field in detail. For this reason, approaches, datasets and the difficulties faced by the researchers in their studies in this field are discussed in the scope of the study. The effect of deep learning and classical approaches on ASR was investigated. A road map is provided for researchers to incorporate the detailed information necessary for their work in this field to their own work and to overcome the present challenges.

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Section: Asr'de Kendi Kendine öğRenme (Self-learning In Asr)unclassified

Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu

Oyucu

Sever

Polat

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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“…The BPNN can be considered as a nonlinear function, the input values and the predicted values represent the independent variables and dependent variables respectively. When the number of the input nodes and the output is n and m respectively, and then the BPNN expresses a functional mapping relationship with n independent variables and m dependent variables [4].…”

Section: A Bp Neural Networkmentioning

confidence: 99%

Research on Music Classification Based on MFCC and BP Neural Network

Chun¹,

Song²,

Yang³

2014

Advances in Intelligent Systems Research

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Because of the diversity and uncertainty of music, the classification rate and accuracy are both lower for the traditional classification methods in the large-scale music classification application. A based on BP neural network (BPNN) music classification method proposed in this paper can improve this performance, which extracts the feature parameters of music through mel frequency cepstrum coefficient(MFCC) firstly, and then the BPNN is used to train feature signals and establish the optimal classifier model, finally classifies the test music dataset. The average classification accuracy rate is up to 90.2%, and higher 7% than the HMM classification method by simulation experiments for the folk, classical, rock and pop different types of music, therefore, the results show that the BPNN is a quite effective music type classification method.

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“…For speech signals, most of the results are focused on speaker recognition and identification [5,6,15,20]. For the existed content authentication schemes, there are shortcomings more or less.…”

Section: Introductionmentioning

confidence: 99%

Tamper recovery algorithm for digital speech signal based on DWT and DCT

Liu

Luo

Huang

et al. 2016

Multimed Tools Appl

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It is a challenging work to design tamper recovery schemes for digital speech signal. Briefly, there are two problems need to be solved. One is that the signals used to tamper recovery are difficult to generate and embed, and the second is that it's hard to tamper location precisely for attacked speech signal. In this paper, compression and reconstruction method based on discrete wavelet transform (DWT) and discrete cosine transform (DCT) is given, to obtain the compressed signals used to tamper recovery. And then frame number and compressed signals are embedded based on block-based method. Attacked signal can be located by frame number, and compressed signals are extracted and used to reconstruct the attacked signal. Theory analysis and experimental results indicate that the scheme proposed not only improves the accuracy of tamper localization, but also can reconstruct the attacked signals.

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Self-learning speaker identification for enhanced speech recognition

Cited by 19 publications

References 11 publications

Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu

Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu

Research on Music Classification Based on MFCC and BP Neural Network

Tamper recovery algorithm for digital speech signal based on DWT and DCT

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