Speaker identification employing waveform based speech CODEC

Speaker Identification systems have become area of intense research in recent years due to its wide range of applications in voice matching, biometric identification, mobile access security, health care management and transportation. The major challenge in speaker identification is how to provide robust and computationally efficient features that accurately captures speaker's unique identity for higher generalized identification. This paper presents hierarchical classification approach for speaker identification using robust time domain features. In hierarchical classification approach, the first level classifier identifies the gender voice (i.e. male or female voice). In addition, the second level classifier identifies the specific speaker voice. The experiments were conducted using the speech data collected from ten subjects including male and female to ensure the generalized model evaluations. Several time domain features were extracted from the collected dataset that were proven highly discriminative for gender identification and specific speaker identification. The proposed method helps to reduce computational time with increase speaker recognition rate. The experimental results obtained highest accuracy of 96.9% using random forest classifier for gender identification. Moreover, for specific speaker identification the highest accuracy of 78% was observed in male speaker identification and 88.7% accuracy was obtained in female speaker identification using random forest classifier. The encouraging results of our experiments show the impact of hierarchical speaker identification using carefully selected time domain features.

This paper concerns the problem of the effect of emotional change on humans and machines for speaker identification. A contrasting experiment is carried out between Automatic Speaker Identification (ASI) system (applying GMM-UBM and Emotional Factor Analysis (EFA) algorithm) and aural system on emotional speech corpus MASC. The experimental result is similar to that in channel-mismatched condition, i.e. the ASI system is much better than the single listener, especially when emotion compensation algorithm EFA is applied. Meanwhile, fusion of multiple listeners can significantly improve the aural system performance by 23.86% and make it outperform the ASI system.

Results of an experimental study and the optimization of features for a conventional vector-quantization codebook-based automatic speaker identification (ASI) system are presented. Standard LPC (linear predictive coding) and a perceptually weighted feature termed PLP (perceptually based linear prediction) are compared using appropriate distance measures, namely, the log-likelihood, and three cepstral variants: constant weighting, the robot-power-sum, and the inverse variance. PLP features combined with a weighted cepstral measure are found to be consistently the best in a number of different digit-independent ASI experiments. Results support the hypothesis that the higher orders of PLP (>5) contain significant speaker-specific information, with ASI performance improving rapidly up to order 8, and then far more slowly yet consistently up to order 16. A similar pattern is seen for codebook size, with fast improvements up to size 64, with more gradual gains thereafter. >

음성코딩 시 성도는 Linear Predictive Coding (LPC) 계수를 이용해서 모델링 한다. 일반적으로 LPC 계수는 양자화와 선형보간 관점에서 유리한 Line Spectral Frequency (LSF) 파라미터로 변경하여 사용한다. 10차 이상의 다차원 LSF 데이터를 벡터 양자화를 이용하여 직접 코딩하게 되면 벡터 내 상관관계 (intra-frame correlation)를 모두 이용할 수 있으므로 rate-distortion 관점에서는 높은 효율을 기대할 수 있다. 하지만, 계산량과 메모리 요구량이 높아져서 실제 코딩 시스템에서는 사용할 수 없게 되므로, 차원을 나누어 압축하는 Split Vector Quantization (SVQ)이 이용된다. 또한, LSF 데이터는 과거 벡터와의 벡터 간 상관관계 (inter-frame correlation)가 높으므로, 이를 이용한 Predictive Split Vector Quantization (PSVQ)이 사용되고 있다. PSVQ는 SVQ 보다 높은 rate-distortion 성능을 보인다. 본 논문에서는 음성 저장 장치를 위한 최적의 PSVQ를 구현하기 위해서 다수의 과거 프레임 정보와의 벡터 간상관관계 (inter-frame correlation)를 고려한 Multi-Frame AR-model 기반 SVQ (MF-AR-SVQ)를 제안하였다. 기존 PSVQ와 비교해 보았을 때, MF-AR-SVQ는 계산량과 메모리 요구량의 큰 증가 없이, 평균 spectral distortion 관점에서 약 1비트의 성능 향상을 보였다. For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

Mismatch between training and testing data is a major error source for both automatic speech recognition (ASR) and automatic speaker identification (ASI). In this paper, we first present a statistical weighting concept to exploit the unequal sensitivity of mel-frequency cepstral coefficients (MFCC) components to against the mismatch, such as ambient noise, recording equipment, transmission channels, and inter-speaker variations. We further design a new Kullback-Leibler (KL) distance based weighting algorithm according to the proposed weighting concept to real-world problems in which the label information is often not provided. We examine our algorithm in ASR with mismatch by different speakers and also in ASI with mismatch by channel noises. Experimental results demonstrate the effectiveness and robustness of our proposed method.

Biometric template protection of speech signals and information hiding in speech signals are two challenging issues. To resolve such limitations and increase the level of security, our objective is to build multi-level security systems based on speech signals. So, speech watermarking is used simultaneously with automatic speaker identification. The speech watermarking is performed to embed images into the speech signals that are used for speaker identification. The watermark is extracted for authentication, and then the effect of watermark removal on the performance of the speaker identification system in the presence of degradations is studied. This paper presents an approach for speech watermarking based on empirical mode decomposition (EMD) in different transform domains and singular value decomposition (SVD). The speech signal is decomposed in different transform domains with EMD to yield zero-mean components called intrinsic mode functions (IMFs). The watermark is inserted into one of these IMF components with SVD. A comparison between different transform domains for implementing the proposed watermarking scheme on different IMFs is presented. The log-likelihood ratio (LLR), correlation coefficient (Cr), signal-to-noise ratio (SNR), and spectral distortion (SD) are used as metrics for the comparison. According to the simulation results, we find that the watermark embedding in the discrete sine transform domain provides higher SNR and Cr values and lower SD and LLR values. The proposed approach is robust to different attacks.

This paper proposes the utilization of SVD digital audio watermarking to increase the security of automatic speaker identification (ASI) systems and presents a study for the effect of watermarking on the ASI system performance. The SVD audio watermarking algorithm can be implemented on audio signals in time domain or in another appropriate transform domain and can be applied to the audio signal as a whole or on a segment-by-segment basis. The speaker recognition system works by generating a database of speaker's features using the MFCCs and polynomial shape coefficients extracted from each speaker after they are lexicographically ordered into 1-D signals. A matching process is performed for any new speaker to determine if he is belonging to the database or not, using a trained neural network. Experimental results show that the SVD audio watermarking doesn't degrade the ASI system performance severely. So, it can be used with ASI to increase security. Also, it was shown the segment by segment watermarking in the time domain achieves the highest detectability of the watermark. So, we can say that it is recommended to use SVD segment by segment audio watermarking with ASI systems implementing features extracted from the DCT or the DWT.

While few individuals would argue that vocal cues can signal a person’s identity, it is difficult to specify exactly which parameter(s) provide the most salient information for speaker identification. Previous literature has suggested that speaking fundamental frequency, long-term spectra, vowel formant frequencies, and speech tempo can provide speaker-specific information. However, investigations focused on automatic speaker identification have provided less than satisfactory results. These findings could be related to how each acoustic parameter is measured or, more probably, to the idea that these acoustic parameters interact in specific ways that may be more obvious in the perceptual realm and may vary across speaking situations. To further complicate matters, individuals may speak more than one language or use multiple dialects. Little is known about the effect of code switching on voice production and identification. The purpose of this presentation is to present some of the relevant literature on voice recognition and factors related to misidentification. The role of intraspeaker variability will be discussed with a special emphasis on bilingualism and bidialectalism. Implications for voice production in augmentative and alternative communication devices will be described.

A two stage fuzzy decision classifier for speaker identification

In this paper, we address the use of a vector quantizer (VQ) defined as a linear mapping of a block code (LMBC-VQ) in robust LPC spectrum quantization. Split VQ and multistage VQ of LSF's are considered. We demonstrate several properties for the LMBC-VQ including design methods and channel robustness theory. The LMBC-VQ codebooks are, guided by the robustness theory, directly designed with inherent robustness against channel errors. We demonstrate results where the resulting LMBC-VQ's outperform conventional codebooks designed with the generalized Lloyd algorithm in the presence of channel errors even when postprocessing index assignment is applied to the latter. Listening tests confirmed that the increased robustness is significant for the perceptual quality. We show that short block codes can be used to build the codebooks with no or small losses in quantization performance. This is advantageous for channel error robustness, lower memory requirement, and lower complexity. We also describe the structure of split VQ and multistage VQ using the LMBC framework. This allows us to assess the structural constraints and to generalize these VQ schemes, resulting in improved performance.

For low bit rate linear predictive speech coding, it is desirable to quantize the LPC parameters using as few bits as possible with transparent quantization quality. A novel structural constrained vector quantization scheme, namely split residual vector quantization (SRVQ), is proposed. In this scheme, PARCOR coefficient vector is split into subvectors and each subvector is sequentially quantized according to a multi-stage structure based on the principle of minimizing the prediction error energies. Simulation results show that a 2-stage split residual vector quantizer can achieve transparent quantization at 25 bit/frame while autocorrelation analysis is employed to calculate the LPC parameters. >

Quantization of data is performed to reduce the bit rate required either for storage of data, or transmission between two or more communicators. Linear prediction (LP) is an efficient way to represent the short term spectrum of the speech signal. The line spectral frequency (LSF) transformation provides benefits over than linear prediction representations such as reflection coefficients, arc sine reflection coefficients, or log area ratios. Vector quantization of the filter parameters allows for a larger reduction in the bit rate to represent a set of parameters over scalar quantization. This reduction comes at the expense of using more computational complexity and greater amounts of storage. To reduce the burden of both storage and computation, various techniques and procedures have been developed. These techniques include full, multistage and split vector quantization, as well as the adaptive, variable dimension and finite state vector quantizers [1-8].

ABSTRACT: Speech is one of the most natural and efficient forms of communication among humans, and it is becoming increasingly integral to human-computer interaction in modern applications such as virtual assistants, smart devices, and automated customer service systems. Among the various technologies that leverage speech, speaker recognition stands out as a biometric method that identifies or verifies individuals based on the unique characteristics of their voice. This approach has gained significant traction due to its non-intrusive nature, ease of integration, and applicability across various domains such as security, forensics, and personalised user experiences. In this paper, we introduce a novel speaker recognition system specifically designed to identify speakers based on utterances in the Marathi language, a linguistically rich and widely spoken regional language in India. The system utilises Mel-Frequency Cepstral Coefficients (MFCCS) to extract distinguishing vocal features that closely mimic the human auditory perception. MFCCS are particularly effective in capturing speech’s phonetic and acoustic properties, making them a preferred choice in speech and speaker recognition tasks. Vector quantisation (VQ) is applied to optimise the feature set and reduce computational complexity. VQ compresses the high-dimensional MFCC feature vectors into representative clusters without significantly compromising accuracy, thereby enhancing system efficiency. These features are then processed using hidden Markov Models (HMMS), which are adept at modelling temporal sequences and dynamic variations in speech. HMMS offer a statistical framework that effectively captures the sequential nature of speech patterns, leading to more reliable speaker modelling and recognition.

Enhancements to DTW and VQ decision algorithms for speaker recognition

The authors present an improved multistate time delay neural network (MS-TDNN) for speaker-independent, connected letter recognition which outperforms an HMM (hidden Markov model) based system (SPHINX) and previous MS-TDNNs. They also explore new network architectures with internal speaker models. Four different architectures characterized by an increasing number of speaker-specific parameters are introduced. The speaker-specific parameters can be adjusted by automatic speaker identification or by speaker adaptation, allowing for tuning-in to a new speaker. Both methods lead to significant improvements over the straightforward speaker-independent architecture. Even unsupervised tuning-in (speech is unlabeled) works well. >