Improving Speaker Identification Performance Under the Shouted Talking Condition Using the Second-Order Hidden Markov Mo
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Improving Speaker Identification Performance Under the Shouted Talking Condition Using the Second-Order Hidden Markov Models Ismail Shahin Electrical/Electronics and Computer Engineering Department, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates Email: [email protected] Received 13 June 2004; Revised 22 September 2004; Recommended for Publication by Chin-Hui Lee 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 23%. On the other hand, the average speaker identification performance based on HMM2s is 59%. Keywords and phrases: first-order hidden Markov models, second-order hidden Markov models, shouted talking condition, speaker identification performance.
1.
MOTIVATION
Stressful talking conditions are defined as talking conditions that cause a speaker to vary his/her production of speech from the neutral talking condition. The neutral talking condition is defined as the talking condition in which speech is produced assuming that the speaker is in a “quiet room” with no task obligations. Some talking conditions are designed to simulate speech produced by different speakers under real stressful talking conditions. Hansen, Cummings, and Clements used speech under simulated and actual stress (SUSAS) database in which eight talking styles are used to simulate the speech produced under real stressful talking conditions and three real talking conditions [1, 2, 3]. The eight conditions are as follows: neutral, loud, soft, angry, fast, slow, clear, and question. The three conditions are 50% task, 70% task and Lombard. Chen used six talking conditions to simulate speech under real stressful talking conditions [4]. These conditions are as follows: neutral, fast, loud, Lombard, soft, and shouted. Most published works in the areas of speech recognition and speaker recognition focus on speech under the neutral talking condition and few published works focus on speech under stressful talking conditions. The vast majority of the studies that focus on speech under stressful talking conditions ignore the shouted talking condition [4, 5, 6]. The shouted talking condition can be defined as follows: when a speaker shouts, his/her object is to produce a very loud
acoustic signal to increase either its range (distance) of transmission or its ratio to background noise. 2.
INTRODUCTION
Hidden Markov model (HMM) is one of the most widely used modeling techniques in the fields of speech recognition and speaker recognition [7]. HMMs use Markov chain to model
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