The Influence of Lombard Effect on Speech Recognition

The origin of Lombard effect dates back one hundred years. In 1911 Etienne Lombard discovered the psychological effect of speech produced in the presence of noise (Lombard, 1911). The Lombard effect is a phenomenon in which speakers increase their vocal levels in the presence of a loud background noise and make several vocal changes in order t

Effective Pitch Value Detection in Noisy Intelligent Environments for Efficient Natural Language Processing

The performance of applications based on natural language processing depends primarily on the environment in which these applications are applied. Intelligent environments will be one of the major applications used to process natural language. The methods for speaker’s gender classification can adapt and improve the performance of natural language processing applications. That is why, this chapter will present an effective speaker’s pitch value detection in noisy environments, which then allows more robust speaker’s gender classification. The chapter presents the algorithm for the speaker’s pitch value detection and performs the comparison in various noisy environments. The experiments are carried out on the part of the publically available Aurora 2 speech database. The results showed that the automatically determined pitch values deviate, on average, only by 8.39 Hz from the reference pitch value. A well-defined pitch value allows a functional speaker’s gender classification. In this chapter, presented speaker’s gender classification works well, even at low signal to noise ratios. The experiments show that the speaker’s gender classification performance at SNR 0 dB is higher than 91% when the automatically determined pitch value is used. Speaker’s gender classification can then be used further in the processes of natural language processing

Acoustic Gender and Age Classification as an Aid to Human–Computer Interaction in a Smart Home Environment

The advanced smart home environment presents an important trend for the future of human wellbeing. One of the prerequisites for applying its rich functionality is the ability to differentiate between various user categories, such as gender, age, speakers, etc. We propose a model for an efficient acoustic gender and age classification system for human–computer interaction in a smart home. The objective was to improve acoustic classification without using high-complexity feature extraction. This was realized with pitch as an additional feature, combined with additional acoustic modeling approaches. In the first step, the classification is based on Gaussian mixture models. In thesecond step, two new procedures are introduced for gender and age classification. The first is based on the count of the frames with the speaker’s pitch values, and the second is based on the sum of the frames with pitch values belonging to a certain speaker. Since both procedures are based on pitch values, we have proposed a new, effective algorithm for pitch value calculation. In order to improve gender and age classification, we also incorporated speech segmentation with the proposed voice activity detection algorithm. We also propose a procedure that enables the quick adaptation of the classification algorithm to frequent smart home users. The proposed classification model with pitch values has improved the results in comparison with the baseline system

A Computationally Efficient Mel-Filter Bank VAD Algorithm for Distributed Speech Recognition Systems

This paper presents a novel computationally efficient voice activity detection (VAD) algorithm and emphasizes the importance of such algorithms in distributed speech recognition (DSR) systems. When using VAD algorithms in telecommunication systems, the required capacity of the speech transmission channel can be reduced if only the speech parts of the signal are transmitted. A similar objective can be adopted in DSR systems, where the nonspeech parameters are not sent over the transmission channel. A novel approach is proposed for VAD decisions based on mel-filter bank (MFB) outputs with the so-called Hangover criterion. Comparative tests are presented between the presented MFB VAD algorithm and three VAD algorithms used in the G.729, G.723.1, and DSR (advanced front-end) Standards. These tests were made on the Aurora 2 database, with different signal-to-noise (SNRs) ratios. In the speech recognition tests, the proposed MFB VAD outperformed all the three VAD algorithms used in the standards by relative (G.723.1 VAD), by relative (G.729 VAD), and by relative (DSR VAD) in all SNRs.</p