Speaker verification is the process by which a speakers claim of identity is tested against a claimed speaker by his or her voice. Speaker verification is done by the use of some parameters (features) from the speakers voice which can be used to differentiate among many speakers. The efficiency of speaker verification system mainly depends on the feature set providing high inter-speaker variability and low intra-speaker variability. There are many methods used for speaker verification. Some systems use Mel Frequency Cepstral Coefficients as features (MFCCs), while others use Hidden Markov Models (HMM) based speaker recognition, Support Vector Machines (SVM), GMMs . In this paper simple intra-pitch temporal information in conjunction with pitch synchronous cepstral coefficients forms the feature set. The distinct feature of a speaker is determined from the steady state part of five cardinal spoken English vowels. The performance was found to be average when these features were used independently. But very encouraging results were observed when both features were combined to form a decision for speaker verification. For a database of twenty speakers of 100 utterances per speaker, an accuracy of 91.04% has been observed. The analysis of speakers whose recognition was incorrect is conducted and discussed .
We propose a new feature, namely, pitchsynchronous discrete cosine transform (PS-DCT), for the task of speaker identification. These features are obtained directly from the voiced segments of the speech signal, without any preemphasis or windowing. The feature vectors are vector quantized, to create one separate codebook for each speaker during training. The performance of the PS-DCT features is shown to be good, and hence it can be used to supplement other features for the speaker identification task. Speaker identification is also performed using Mel-frequency cepstral coefficient (MFCC) fea…
Due to improvements in artificial intelligence, speaker identification (SI) technologies have brought a great direction and are now widely used in a variety of sectors. One of the most important components of SI is feature extraction, which has a substantial impact on the SI process and performance. As a result, numerous feature extraction strategies are thoroughly investigated, contrasted, and analyzed. This article exploits five distinct feature extraction methods for speaker identification in disguised voices under emotional environments. To evaluate this work significantly, three effects a…
Indian Institute of Science（印度科学理工学院）；Intel Technology India Pvt Ltd（英特尔科技印度私人有限公司）；National University of Singapore（新加坡国立大学）
Time- and pitch-scale modifications of speech signals find important applications in speech synthesis, playback systems, voice conversion, learning/hearing aids, etc.. There is a requirement for computationally efficient and real-time implementable algorithms. In this paper, we propose a high quality and computationally efficient time- and pitch-scaling methodology based on the glottal closure instants (GCIs) or epochs in speech signals. The proposed algorithm, termed as epoch-synchronous overlap-add time/pitch-scaling (ESOLA-TS/PS), segments speech signals into overlapping short-time frames a…
Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）
Feature extraction plays an important role as a front-end processing block in speaker identification (SI) process. Most of the SI systems utilize like Mel-Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), Linear Predictive Cepstral Coefficients (LPCC), as a feature for representing speech signal. Their derivations are based on short term processing of speech signal and they try to capture the vocal tract information ignoring the contribution from the vocal cord. Vocal cord cues are equally important in SI context, as the information like pitch frequency, phase in the …
Faculté Polytechnique de Mons（蒙斯理工学院）；Acapela Group（Acapela集团）
This paper proposes a method to improve the quality delivered by statistical parametric speech synthesizers. For this, we use a codebook of pitch-synchronous residual frames, so as to construct a more realistic source signal. First a limited codebook of typical excitations is built from some training database. During the synthesis part, HMMs are used to generate filter and source coefficients. The latter coefficients contain both the pitch and a compact representation of target residual frames. The source signal is obtained by concatenating excitation frames picked up from the codebook, based …
Convolutional neural networks (CNN) are widely used for speech emotion recognition (SER). In such cases, the short time fourier transform (STFT) spectrogram is the most popular choice for representing speech, which is fed as input to the CNN. However, the uncertainty principles of the short-time Fourier transform prevent it from capturing time and frequency resolutions simultaneously. On the other hand, the recently proposed single frequency filtering (SFF) spectrogram promises to be a better alternative because it captures both time and frequency resolutions simultaneously. In this work, we e…
Aalto University（阿尔托大学）；National Institute of Informatics（国立信息学研究所）；University of Edinburgh（爱丁堡大学）
The state-of-the-art in text-to-speech synthesis has recently improved considerably due to novel neural waveform generation methods, such as WaveNet. However, these methods suffer from their slow sequential inference process, while their parallel versions are difficult to train and even more expensive computationally. Meanwhile, generative adversarial networks (GANs) have achieved impressive results in image generation and are making their way into audio applications; parallel inference is among their lucrative properties. By adopting recent advances in GAN training techniques, this investigat…
Comparative Analysis of Mel-Frequency Cepstral Coefficients and Wavelet Based Audio Signal Processing for Emotion Detection and Mental Health Assessment in Spoken Speech
Inria（法国国家信息与自动化研究所）；EHESS（法国高等社会科学研究院）；ENS（巴黎高等师范学院）；PSL Research University（巴黎文理研究大学）；CNRS（法国国家科学研究中心）；Facebook AI Research（脸书人工智能研究院）
We introduce Shennong, a Python toolbox and command-line utility for speech features extraction. It implements a wide range of well-established state of art algorithms including spectro-temporal filters such as Mel-Frequency Cepstral Filterbanks or Predictive Linear Filters, pre-trained neural networks, pitch estimators as well as speaker normalization methods and post-processing algorithms. Shennong is an open source, easy-to-use, reliable and extensible framework. The use of Python makes the integration to others speech modeling and machine learning tools easy. It aims to replace or compleme…
Existing singing voice synthesis models (SVS) are usually trained on singing data and depend on either error-prone time-alignment and duration features or explicit music score information. In this paper, we propose Karaoker, a multispeaker Tacotron-based model conditioned on voice characteristic features that is trained exclusively on spoken data without requiring time-alignments. Karaoker synthesizes singing voice and transfers style following a multi-dimensional template extracted from a source waveform of an unseen singer/speaker. The model is jointly conditioned with a single deep convolut…
Faculty of Engineering and Technology（工程与技术学院）；Faculty of Audiology and Speech Language Pathology（听觉与语言病理学学院）；Sri Ramachandra Institute of Higher Education and Research（Sri Ramachandra 高等教育与研究学院）
Speech Emotion Recognition Using MFCC Features and LSTM-Based Deep Learning Model
Department of Electrical Electronics and Telecommunications Engineering, Bells University of Technology（贝尔大学技术学院电子与电信工程系）；New Horizons Bells University of Technology（新希望贝尔大学技术学院）；Department of Computer Engineering, Bells University of Technology（贝尔大学技术学院计算机工程系）；Department of Computer and Information Sciences, Towson University（托逊大学计算机与信息科学系）
National Institute of Technology Patna（巴特那国家技术学院）；Central University of South Bihar（南比哈尔中央大学）
Speech is produced when time varying vocal tract system is excited with time varying excitation source. Therefore, the information present in a speech such as message, emotion, language, speaker is due to the combined effect of both excitation source and vocal tract system. However, there is very less utilization of excitation source features to recognize emotion. In our earlier work, we have proposed a novel method to extract glottal closure instants (GCIs) known as epochs. In this paper, we have explored epoch features namely instantaneous pitch, phase and strength of epochs for discriminati…
