David C. Wyld et al. (Eds) : CSITY, SIGPRO, DTMN - 2015 pp. 23–30, 2015. © CS & IT-CSCP 2015 DOI : 10.5121/csit.2015.50303 HINDI DIGITS RECOGNITION SYSTEM ON SPEECH DATA COLLECTED IN DIFFERENT NATURAL NOISE ENVIRONMENTS Babita Saxena 1 and Charu Wahi 2 Department of Computer Science, Birla Institute of Technology, Noida babita.gs@gmail.com charu@bitmesra.com ABSTRACT This paper presents a baseline digits speech recognizer for Hindi language. The recording environment is different for all speakers, since the data is collected in their respective homes. The different environment refers to vehicle horn noises in some road facing rooms, internal background noises in some rooms like opening doors, silence in some rooms etc. All these recordings are used for training acoustic model. The Acoustic Model is trained on 8 speakers’ audio data. The vocabulary size of the recognizer is 10 words. HTK toolkit is used for building acoustic model and evaluating the recognition rate of the recognizer. The efficiency of the recognizer developed on recorded data, is shown at the end of the paper and possible directions for future research work are suggested. KEYWORDS HMM, Acoustic Model, Digit Speech Recognition, Grammar 1. INTRODUCTION In the last few years, Hidden-Markov-Model based (HMM) algorithms have been the most successful techniques used for speech recognition systems. Using the same, the experiments are conducted for building a Digit Speech Recognition(DSR) for Hindi. Thus, for building a DSR, acoustic characteristics like pitch, formant frequencies etc have to be computed. These characteristics are captured and a model is built based on these. These models are further used for recognition purposes. In this paper we present our work on building acoustic model for Hindi Digits. Hindi belongs to the Indo Aryan family of languages and is written in the devanagari script. There are 11 vowels and 35 consonants in standard Hindi. In addition, 5 Nukta consonants are also adopted from Farsi/Arabic sounds.