Real Time SpeakerRecognition on TMS320C6713

Speaker recognition is defined as the process of identifying a person on the basis of the information contained in speech.In this world where breach of security is a major threat ,speaker recognition is one of the major biometric recognition techniques. A large number of organizations like banks,defence laboratories ,industries , forensic surveillance are using this technology for security purposes.Speaker recognition is mainly divided into two categories : Speaker identification andSpeaker verification. In speaker identification we find out which speaker has uttered the given speech ,whereas in speaker verification we determine if the speaker who is claiming a particular identity is telling the truth or not.In our first phase we did speaker recognition on MATLAB.The process we followed comprised of three parts.First we did preprocessing where we truncated the signal and performed thresholding on it.Then we extracted the features of speech signals using Mel frequency Cepstrum coefficients. These extracted features were then matched with a set of speakers using a Vector Quantization approach.In our second phase we tried to implement speaker recognition in real time.As speaker recognition is a signal processing task ,we decided to implement it real time on a DSP (digital signl processor) as it performs very fast multiply and accumulate operations(MAC) and speaker recognition had stages where signals were primarily added and multiplied .Hence DSP was choosen as our platform.The second phase comprises our familiarisation with the TMS320C6713 DSP,the first few audio applications we performed on it,some real time filters we developed on it and finally our speech recognition problem

Real time speaker recognition using MFCC and VQ

Speaker Recognition is a process of automatically recognizing who is speaking on the basis of the individual information included in speech waves. Speaker Recognition is one of the most useful biometric recognition techniques in this world where insecurity is a major threat. Many organizations like banks, institutions, industries etc are currently using this technology for providing greater security to their vast databases.Speaker Recognition mainly involves two modules namely feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the speaker’s voice signal that can later be used to represent that speaker. Feature matching involves the actual procedure to identify the unknown speaker by comparing the extracted features from his/her voice input with the ones that are already stored in our speech database.In feature extraction we find the Mel Frequency Cepstrum Coefficients, which are based on the known variation of the human ear’s critical bandwidths with frequency and these, are vector quantized using LBG algorithm resulting in the speaker specific codebook. In feature matching we find the VQ distortion between the input utterance of an unknown speaker and the codebooks stored in our database. Based on this VQ distortion we decide whether to accept/reject the unknown speaker’s identity. The system I implemented in my work is 80% accurate in recognizing the correct speaker.In second phase we implement on the acoustic of Real Time speaker ecognition using mfcc and vq on a TMS320C6713 DSP board. We analyze the workload and identify the most timeconsuming operations