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

Deep Multimodal Speaker Naming

Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online

The right information may matter more than frequency-place alignment: Simulations of frequency-aligned and upward shifting cochlear implant processors for a shallow electrode array insertion

Objective: It has been claimed that speech recognition with a cochlear implant is dependent on the correct frequency alignment of analysis bands in the speech processor with characteristic frequencies (CFs) at electrode locations. However, the use of filters aligned in frequency to a relatively basal electrode array position leads to significant loss of lower frequency speech information. This study uses an acoustic simulation to compare two approaches to the matching of speech processor filters to an electrode array having a relatively shallow depth within the typical range, such that the most apical element is at a CF of 1851 Hz. Two noise-excited vocoder speech processors are compared, one with CF-matched filters, and one with filters matched to CFs at basilar membrane locations 6 mm more apical than electrode locations.Design: An extended crossover training design examined pre- and post-training performance in the identification of vowels and words in sentences for both processors. Subjects received about 3 hours of training with each processor in turn.Results: Training improved performance with both processors, but training effects were greater for the shifted processor. For a male talker, the shifted processor led to higher post-training scores than the frequency-aligned processor with both vowels and sentences. For a female talker, post-training vowel scores did not differ significantly between processors, whereas sentence scores were higher with the frequency-aligned processor.Conclusions: Even for a shallow electrode insertion, we conclude that a speech processor should represent information from important frequency regions below 1 kHz and that the possible cost of frequency misalignment can be significantly reduced with listening experience

On Using Backpropagation for Speech Texture Generation and Voice Conversion

Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthesis and voice conversion based on two mechanisms: approximate inversion of the representation learned by a speech recognition neural network, and on matching statistics of neuron activations between different source and target utterances. Similar to image texture synthesis and neural style transfer, the system works by optimizing a cost function with respect to the input waveform samples. To this end we use a differentiable mel-filterbank feature extraction pipeline and train a convolutional CTC speech recognition network. Our system is able to extract speaker characteristics from very limited amounts of target speaker data, as little as a few seconds, and can be used to generate realistic speech babble or reconstruct an utterance in a different voice.Comment: Accepted to ICASSP 201

Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech

The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One prominent clinical application is a computer-assisted speech training system which enables personalized speech therapy to patients impaired by communicative disorders in the patient's home environment. Such a system relies on the robust automatic speech recognition (ASR) technology to be able to provide accurate articulation feedback. With the long-term aim of developing off-the-shelf ASR systems that can be incorporated in clinical context without prior speaker information, we compare the ASR performance of speaker-independent bottleneck and articulatory features on dysarthric speech used in conjunction with dedicated neural network-based acoustic models that have been shown to be robust against spectrotemporal deviations. We report ASR performance of these systems on two dysarthric speech datasets of different characteristics to quantify the achieved performance gains. Despite the remaining performance gap between the dysarthric and normal speech, significant improvements have been reported on both datasets using speaker-independent ASR architectures.Comment: to appear in Computer Speech & Language - https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial text overlap with arXiv:1807.1094

Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database