Ismail Shahin

Speaker identification in the shouted environment using Suprasegmental Hidden Markov Models

In this paper, Suprasegmental Hidden Markov Models (SPHMMs) have been used to enhance the recognition performance of text-dependent speaker identification in the shouted environment. Our speech database consists of two databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. Our results show that SPHMMs significantly enhance speaker identification performance compared to Second-Order Circular Hidden Markov Models (CHMM2s) in the shouted environment. Using our collected database, speaker identification performance in this environment is 68% and 75% based on CHMM2s and SPHMMs, respectively. Using the SUSAS database, speaker identification performance in the same environment is 71% and 79% based on CHMM2s and SPHMMs, respectively.

Enhancing speaker identification performance under the shouted talking condition using second-order circular hidden Markov models

It is known that the performance of speaker identification systems is high under the neutral talking condition; however, the performance deteriorates under the shouted talking condition. In this paper, second-order circular hidden Markov models (CHMM2s) have been proposed and implemented to enhance the performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that CHMM2s significantly improve speaker identification performance under such a condition compared to the first-order left-to-right hidden Markov models (LTRHMM1s), second-order left-to-right hidden Markov models (LTRHMM2s), and the first-order circular hidden Markov models (CHMM1s). Under the shouted talking condition, our results show that the average speaker identification performance is 23% based on LTRHMM1s, 59% based on LTRHMM2s, and 60% based on CHMM1s. On the other hand, the average speaker identification performance under the same talking condition based on CHMM2s is 72%.

Improving speaker identification performance under the shouted talking condition using the second-order hidden Markov models

Speaker identification systems perform well under the neutral talking condition; however, they suffer sharp degradation under the shouted talking condition. In this paper, the second-order hidden Markov models (HMM2s) have been used to improve the recognition performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that HMM2s significantly improve the speaker identification performance compared to the first-order hidden Markov models (HMM1s). The average speaker identification performance under the shouted talking condition based on HMM1s is. On the other hand, the average speaker identification performance based on HMM2s is.

Speaker identification in emotional talking environments based on CSPHMM2s

Speaker recognition systems perform almost ideal in neutral talking environments; however, these systems perform poorly in emotional talking environments. This research is devoted to enhancing the low performance of text-independent and emotion-dependent speaker identification in emotional talking environments based on employing Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) as classifiers. This work has been tested on our speech database which is composed of 50 speakers talking in six different emotional states. These states are neutral, angry, sad, happy, disgust, and fear. Our results show that the average speaker identification performance in these talking environments based on CSPHMM2s is 81.50% with an improvement rate of 5.61%, 3.39%, and 3.06% compared, respectively, to First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM1s), Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM2s), and First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s). Our results based on subjective evaluation by human judges fall within 2.26% of those obtained based on CSPHMM2s.

Identifying speakers using their emotion cues

This paper addresses the formulation of a new speaker identification approach which employs knowledge of emotional content of speaker information. Our proposed approach in this work is based on a two-stage recognizer that combines and integrates both emotion recognizer and speaker recognizer into one recognizer. The proposed approach employs both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. In the experiments, six emotions are considered including neutral, angry, sad, happy, disgust and fear. Our results show that average speaker identification performance based on the proposed two-stage recognizer is 79.92% with a significant improvement over a one-stage recognizer with an identification performance of 71.58%. The results obtained based on the proposed approach are close to those achieved in subjective evaluation by human listeners.

Novel third-order hidden Markov models for speaker identification in shouted talking environments

Speaker identification systems perform almost perfectly in neutral talking environments; however, they perform poorly in shouted talking environments. This work aims at proposing, implementing, and evaluating novel models called Third-Order Hidden Markov Models (HMM3s) to enhance the poor performance of text-independent speaker identification systems in shouted talking environments. The proposed models have been evaluated on our collected speech database using Mel-Frequency Cepstral Coefficients (MFCCs). Our results show that HMM3s significantly improve speaker identification performance in shouted talking environments compared to second-order hidden Markov models (HMM2s) and first-order hidden Markov models (HMM1s) by 12.4% and 202.4%, respectively. The achieved results based on the proposed models are close to those obtained in subjective assessment by human listeners.

Employing second-order circular suprasegmental hidden Markov models to enhance speaker identification performance in shouted talking environments

Speaker identification performance is almost perfect in neutral talking environments. However, the performance is deteriorated significantly in shouted talking environments. This work is devoted to proposing, implementing, and evaluating new models called Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) to alleviate the deteriorated performance in the shouted talking environments. These proposed models possess the characteristics of both Circular Suprasegmental Hidden Markov Models (CSPHMMs) and Second-Order Suprasegmental Hidden Markov Models (SPHMM2s). The results of this work show that CSPHMM2s outperform each of First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM1s), Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM2s), and First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s) in the shouted talking environments. In such talking environments and using our collected speech database, average speaker identification performance based on LTRSPHMM1s, LTRSPHMM2s, CSPHMM1s, and CSPHMM2s is 74.6%, 78.4%, 78.7%, and 83.4%, respectively. Speaker identification performance obtained based on CSPHMM2s is close to that obtained based on subjective assessment by human listeners.

Speaker Recognition Systems in the Emotional Environment

It is well known that speaker recognition systems perform extremely well in the neutral environment. However, such systems perform poorly in the emotional environment. Our work in this research focuses on text- dependent speaker identification systems in the emotional environment. Our emotional environment consists of five emotions. These emotions are angry, sad, happy, disgust, and fear. Each of the hidden Markov models (HMMs) and the cepstral mean subtraction technique based on HMMs has been used separately in both the training and testing sessions of such systems. Our results show that speaker identification systems in the emotional environment based on the cepstral mean subtraction technique yield better identification performance than that based on HMMs.

Enhancing speaker identification performance using circular hidden Markov model

In this paper, circular hidden Markov model (CHMM) is implemented to improve the recognition performance of isolated-word text-dependent speaker identification systems under the neural talking condition. Our results show that the CHMM improves the speaker recognition performance under such a condition compared to the left-to-right hidden Markov model (LTRHMM). The average speaker recognition performance has been improved from 90% using the LTRHMM to 95% using the CHMM. In this research, the linear predictive coding (LPC) cepstral feature analysis is used to form the observation vector for both LTRHMM and CHMM.

Employing Emotion Cues to Verify Speakers in Emotional Talking Environments

Abstract Usually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people’s talking tone include happiness, anger, and sadness. Such emotions are directly affected by the patient’s health status. In neutral talking environments, speakers can be easily verified; however, in emotional talking environments, speakers cannot be easily verified as in neutral talking ones. Consequently, speaker verification systems do not perform well in emotional talking environments as they do in neutral talking environments. In this work, a two-stage approach has been employed and evaluated to improve speaker verification performance in emotional talking environments. This approach employs speaker’s emotion cues (text-independent and emotion-dependent speaker verification problem) based on both hidden Markov models (HMMs) and suprasegmental HMMs as classifiers. The approach is composed of two cascaded stages that combine and integrate an emotion recognizer and a speaker recognizer into one recognizer. The architecture has been tested on two different and separate emotional speech databases: our collected database and the Emotional Prosody Speech and Transcripts database. The results of this work show that the proposed approach gives promising results with a significant improvement over previous studies and other approaches such as emotion-independent speaker verification approach and emotion-dependent speaker verification approach based completely on HMMs.