Suprasegmental Hidden Markov Models Research Articles

Speaker Identification in a Shouted Talking Environment Based on Novel Third-Order Circular Suprasegmental Hidden Markov Models

It is well known that speaker identification yields very high performance in a neutral talking environment, on the other hand, the performance has been sharply declined in a shouted talking environment. This work aims at proposing, implementing, and evaluating novel Third-Order Circular Suprasegmental Hidden Markov Models (CSPHMM3s) to improve the low performance of text-independent speaker identification in a shouted talking environment. CSPHMM3s possess combined characteristics of: Circular Hidden Markov Models (CHMMs), Third-Order Hidden Markov Models (HMM3s), and Suprasegmental Hidden Markov Models (SPHMMs). Our results show that CSPHMM3s are superior to each of: First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM1s), Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM2s), Third-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM3s), First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s), and Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) in a shouted talking environment. Using our collected speech database, average speaker identification performance in a shouted talking environment based on LTRSPHMM1s, LTRSPHMM2s, LTRSPHMM3s, CSPHMM1s, CSPHMM2s, and CSPHMM3s is 74.6%, 78.4%, 81.7%, 78.7%, 83.4%, and 85.8%, respectively. Speaker identification performance that has been achieved based on CSPHMM3s is close to that attained based on subjective assessment by human listeners.

Employing Emotion Cues to Verify Speakers in Emotional Talking Environments

AbstractUsually, 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.

Talking condition recognition in stressful and emotional talking environments based on CSPHMM2s

This work is aimed at exploiting Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) as classifiers to enhance talking condition recognition in stressful and emotional talking environments (completely two separate environments). The stressful talking environment that has been used in this work uses Speech Under Simulated and Actual Stress (SUSAS) database, while the emotional talking environment uses Emotional Prosody Speech and Transcripts (EPST) database. The achieved results of this work using Mel-Frequency Cepstral Coefficients (MFCCs) demonstrate that CSPHMM2s outperform each of Hidden Markov Models (HMMs), Second-Order Circular Hidden Markov Models (CHMM2s), and Suprasegmental Hidden Markov Models (SPHMMs) in enhancing talking condition recognition in the stressful and emotional talking environments. The results also show that the performance of talking condition recognition in stressful talking environments leads that in emotional talking environments by 3.67% based on CSPHMM2s. Our results obtained in subjective evaluation by human judges fall within 2.14% and 3.08% of those obtained, respectively, in stressful and emotional talking environments based on CSPHMM2s.

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22%, 4.45%, and 19.56%, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35% of that obtained in subjective evaluation by human judges.

Gender-dependent emotion recognition based on HMMs and SPHMMs

It is well known that emotion recognition performance is not ideal. The work of this research is devoted to improving emotion recognition performance by employing a two-stage recognizer that combines and integrates gender recognizer and emotion recognizer into one system. Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in the two-stage recognizer. This recognizer has been tested on two distinct and separate emotional speech databases. The first database is our collected database and the second one is the Emotional Prosody Speech and Transcripts database. Six basic emotions including the neutral state have been used in each database. Our results show that emotion recognition performance based on the two-stage approach (gender-dependent emotion recognizer) has been significantly improved compared to that based on emotion recognizer without gender information and emotion recognizer with correct gender information by an average of 11% and 5%, respectively. This work shows that the highest emotion identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models. The results achieved based on the two-stage framework fall within 2.28% of those obtained in subjective assessment by human judges.

Speaker identification investigation and analysis in unbiased and biased emotional talking environments

This work aims at investigating and analyzing speaker identification in each unbiased and biased emotional talking environments based on a classifier called Suprasegmental Hidden Markov Models (SPHMMs). The first talking environment is unbiased towards any emotion, while the second talking environment is biased towards different emotions. Each of these talking environments is made up of six distinct emotions. These emotions are neutral, angry, sad, happy, disgust and fear. The investigation and analysis of this work show that speaker identification performance in the biased talking environment is superior to that in the unbiased talking environment. The obtained results in this work are close to those achieved in subjective assessment by human judges.

Studying and enhancing talking condition recognition in stressful and emotional talking environments based on HMMs, CHMM2s and SPHMMs

The work of this research is devoted to studying and enhancing talking condition recognition in stressful and emotional talking environments (completely two separate environments) based on three different and separate classifiers. The three classifiers are: Hidden Markov Models (HMMs), Second-Order Circular Hidden Markov Models (CHMM2s) and Suprasegmental Hidden Markov Models (SPHMMs). The stressful talking environments that have been used in this work are composed of neutral, shouted, slow, loud, soft and fast talking conditions, while the emotional talking environments are made up of neutral, angry, sad, happy, disgust and fear emotions. The achieved results in the current work show that SPHMMs lead each of HMMs and CHMM2s in improving talking condition recognition in stressful and emotional talking environments. The results also demonstrate that talking condition recognition in stressful talking environments outperforms that in emotional talking environments by 2.7%, 1.8% and 3.3% based on HMMs, CHMM2s and SPHMMs, respectively. Based on subjective assessment by human judges, the recognition performance of stressful talking conditions leads that of emotional ones by 5.2%.

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.

Speaker Identification in Each of the Neutral and Shouted Talking Environments Based on Gender-Dependent Approach Using Sphmms

It is well known that speaker identification performs extremely well in the neutral talking environments; however, the identification performance is declined sharply in the shouted talking environments. This work aims at proposing, implementing and testing a new approach to enhance the declined performance in the shouted talking environments. The new proposed approach is based on gender-dependent speaker identification using suprasegmental hidden Markov models (SPHMMs) as classifiers. This proposed approach has been tested on two different and separate speech databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. The results of this work show that gender-dependent speaker identification based on SPHMMs outperforms gender-independent speaker identification based on the same models and gender-dependent speaker identification based on hidden Markov models (HMMs) by about 6 and 8%, respectively. The results obtained based on the proposed approach are close to those obtained in subjective evaluation by human judges.

Analysis and investigation of emotion identification in biased emotional talking environments

Speakers usually use certain words more frequently in expressing their emotions since they have learned the connection between certain words and their corresponding emotions. The work of this research is devoted to the analysis and investigation of emotion identification in two separate and different talking environments based on classifiers called suprasegmental hidden Markov models. The first talking environment is unbiased towards any emotional state, whereas the second talking environment is biased towards different emotional states. Each emotional talking environment is composed of six emotions. The results of this work show that emotion identification performance in the second talking environment outperforms that in the first talking environment. Based on subjective assessment by human judges, emotion identification performance in the biased talking environment leads that in the unbiased talking environment.

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.