Development of Language Identification using Line Spectral Frequencies and Learning Vector Quantization Networks

Language identification system has become a very active research nowadays due to the need of intercultural human communication. This paper proposed a Language Identification System using Line Spectral Frequencies (LSF) and Linear Vector Quantization (LVQ) network. LSF was used due to its robustness compared to normal linear predictor coefficients (LPC), while LVQ was used due to its low complexity. Three languages, i.e. Arabic, Malay, and Thai, for both native male and female speakers were recorded at IIUM Recording Studio. Several experiments have been conducted to find the optimum parameters, i.e. sampling frequency (8000 Hz), LPC order (18), number of hidden layers (300), and learning rate (0.01). Results show that our proposed system is able to recognize the trained languages with the recognition rate of 73.8%. Further research could be conducted to improve the performance using different features, classifiers, or using deep learning neural network

A Parametric Approach for Efficient Speech Storage, Flexible Synthesis and Voice Conversion

During the past decades, many areas of speech processing have benefited from the vast increases in the available memory sizes and processing power. For example, speech recognizers can be trained with enormous speech databases and high-quality speech synthesizers can generate new speech sentences by concatenating speech units retrieved from a large inventory of speech data. However, even in today's world of ever-increasing memory sizes and computational resources, there are still lots of embedded application scenarios for speech processing techniques where the memory capacities and the processor speeds are very limited. Thus, there is still a clear demand for solutions that can operate with limited resources, e.g., on low-end mobile devices. This thesis introduces a new segmental parametric speech codec referred to as the VLBR codec. The novel proprietary sinusoidal speech codec designed for efficient speech storage is capable of achieving relatively good speech quality at compression ratios beyond the ones offered by the standardized speech coding solutions, i.e., at bitrates of approximately 1 kbps and below. The efficiency of the proposed coding approach is based on model simplifications, mode-based segmental processing, and the method of adaptive downsampling and quantization. The coding efficiency is also further improved using a novel flexible multi-mode matrix quantizer structure and enhanced dynamic codebook reordering. The compression is also facilitated using a new perceptual irrelevancy removal method. The VLBR codec is also applied to text-to-speech synthesis. In particular, the codec is utilized for the compression of unit selection databases and for the parametric concatenation of speech units. It is also shown that the efficiency of the database compression can be further enhanced using speaker-specific retraining of the codec. Moreover, the computational load is significantly decreased using a new compression-motivated scheme for very fast and memory-efficient calculation of concatenation costs, based on techniques and implementations used in the VLBR codec. Finally, the VLBR codec and the related speech synthesis techniques are complemented with voice conversion methods that allow modifying the perceived speaker identity which in turn enables, e.g., cost-efficient creation of new text-to-speech voices. The VLBR-based voice conversion system combines compression with the popular Gaussian mixture model based conversion approach. Furthermore, a novel method is proposed for converting the prosodic aspects of speech. The performance of the VLBR-based voice conversion system is also enhanced using a new approach for mode selection and through explicit control of the degree of voicing. The solutions proposed in the thesis together form a complete system that can be utilized in different ways and configurations. The VLBR codec itself can be utilized, e.g., for efficient compression of audio books, and the speech synthesis related methods can be used for reducing the footprint and the computational load of concatenative text-to-speech synthesizers to levels required in some embedded applications. The VLBR-based voice conversion techniques can be used to complement the codec both in storage applications and in connection with speech synthesis. It is also possible to only utilize the voice conversion functionality, e.g., in games or other entertainment applications

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Efficient Approaches for Voice Change and Voice Conversion Systems

In this thesis, the study and design of Voice Change and Voice Conversion systems are presented. Particularly, a voice change system manipulates a speaker’s voice to be perceived as it is not spoken by this speaker; and voice conversion system modifies a speaker’s voice, such that it is perceived as being spoken by a target speaker. This thesis mainly includes two sub-parts. The first part is to develop a low latency and low complexity voice change system (i.e. includes frequency/pitch scale modification and formant scale modification algorithms), which can be executed on the smartphones in 2012 with very limited computational capability. Although some low-complexity voice change algorithms have been proposed and studied, the real-time implementations are very rare. According to the experimental results, the proposed voice change system achieves the same quality as the baseline approach but requires much less computational complexity and satisfies the requirement of real-time. Moreover, the proposed system has been implemented in C language and was released as a commercial software application. The second part of this thesis is to investigate a novel low-complexity voice conversion system (i.e. from a source speaker A to a target speaker B) that improves the perceptual quality and identity without introducing large processing latencies. The proposed scheme directly manipulates the spectrum using an effective and physically motivated method – Continuous Frequency Warping and Magnitude Scaling (CFWMS) to guarantee high perceptual naturalness and quality. In addition, a trajectory limitation strategy is proposed to prevent the frame-by-frame discontinuity to further enhance the speech quality. The experimental results show that the proposed method outperforms the conventional baseline solutions in terms of either objective tests or subjective tests

Mapping Techniques for Voice Conversion

Speaker identity plays an important role in human communication. In addition to the linguistic content, speech utterances contain acoustic information of the speaker characteristics. This thesis focuses on voice conversion, a technique that aims at changing the voice of one speaker (a source speaker) into the voice of another specific speaker (a target speaker) without changing the linguistic information. The relationship between the source and target speaker characteristics is learned from the training data. Voice conversion can be used in various applications and fields: text-to-speech systems, dubbing, speech-to-speech translation, games, voice restoration, voice pathology, etc. Voice conversion offers many challenges: which features to extract from speech, how to find linguistic correspondences (alignment) between source and target features, which machine learning techniques to use for creating a mapping function between the features of the speakers, and finally, how to make the desired modifications to the speech waveform. The features can be any parameters that describe the speech and the speaker identity, e.g. spectral envelope, excitation, fundamental frequency, and phone durations. The main focus of the thesis is on the design of suitable mapping techniques between frame-level source and target features, but also aspects related to parallel data alignment and prosody conversion are addressed. The perception of the quality and the success of the identity conversion are largely subjective. Conventional statistical techniques are able to produce good similarity between the original and the converted target voices but the quality is usually degraded. The objective of this thesis is to design conversion techniques that enable successful identity conversion while maintaining the original speech quality. Due to the limited amount of data, statistical techniques are usually utilized in extracting the mapping function. The most popular technique is based on a Gaussian mixture model (GMM). However, conventional GMM-based conversion suffers from many problems that result in degraded speech quality. The problems are analyzed in this thesis, and a technique that combines GMM-based conversion with partial least squares regression is introduced to alleviate these problems. Additionally, approaches to solve the time-independent mapping problem associated with many algorithms are proposed. The most significant contribution of the thesis is the proposed novel dynamic kernel partial least squares regression technique that allows creating a non-linear mapping function and improves temporal correlation. The technique is straightforward, efficient and requires very little tuning. It is shown to outperform the state-of-the-art GMM-based technique using both subjective and objective tests over a variety of speaker pairs. In addition, quality is further improved when aperiodicity and binary voicing values are predicted using the same technique. The vast majority of the existing voice conversion algorithms concern the transformation of the spectral envelopes. However, prosodic features, such as fundamental frequency movements and speaking rhythm, also contain important cues of identity. It is shown in the thesis that pure prosody alone can be used, to some extent, to recognize speakers that are familiar to the listeners. Furthermore, a prosody conversion technique is proposed that transforms fundamental frequency contours and durations at syllable level. The technique is shown to improve similarity to the target speaker’s prosody and reduce roboticness compared to a conventional frame-based conversion technique. Recently, the trend has shifted from text-dependent to text-independent use cases meaning that there is no parallel data available. The techniques proposed in the thesis currently assume parallel data, i.e. that the same texts have been spoken by both speakers. However, excluding the prosody conversion algorithm, the proposed techniques require no phonetic information and are applicable for a small amount of training data. Moreover, many text-independent approaches are based on extracting a sort of alignment as a pre-processing step. Thus the techniques proposed in the thesis can be exploited after the alignment process

Robust speaker identification against computer aided voice impersonation

Speaker Identification (SID) systems offer good performance in the case of noise free speech and most of the on-going research aims at improving their reliability in noisy environments. In ideal operating conditions very low identification error rates can be achieved. The low error rates suggest that SID systems can be used in real-life applications as an extra layer of security along with existing secure layers. They can, for instance, be used alongside a Personal Identification Number (PIN) or passwords. SID systems can also be used by law enforcements agencies as a detection system to track wanted people over voice communications networks. In this thesis, the performance of 'the existing SID systems against impersonation attacks is analysed and strategies to counteract them are discussed. A voice impersonation system is developed using Gaussian Mixture Modelling (GMM) utilizing Line Spectral Frequencies (LSF) as the features representing the spectral parameters of the source-target pair. Voice conversion systems based on probabilistic approaches suffer from the problem of over smoothing of the converted spectrum. A hybrid scheme using Linear Multivariate Regression and GMM, together with posterior probability smoothing is proposed to reduce over smoothing and alleviate the discontinuities in the converted speech. The converted voices are used to intrude a closed-set SID system in the scenarios of identity disguise and targeted speaker impersonation. The results of the intrusion suggest that in their present form the SID systems are vulnerable to deliberate voice conversion attacks. For impostors to transform their voices, a large volume of speech data is required, which may not be easily accessible. In the context of improving the performance of SID against deliberate impersonation attacks, the use of multiple classifiers is explored. Linear Prediction (LP) residual of the speech signal is also analysed for speaker-specific excitation information. A speaker identification system based on multiple classifier system, using features to describe the vocal tract and the LP residual is targeted by the impersonation system. The identification results provide an improvement in rejecting impostor claims when presented with converted voices. It is hoped that the findings in this thesis, can lead to the development of speaker identification systems which are better equipped to deal with the problem with deliberate voice impersonation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Cover, Copy, and Compare: An Effective Strategy for ASL Acquisition for Students with Dyslexia?

A single subject, multiple baseline study was designed to determine the benefits of Skinner’s Cover, Copy, Compare (CCC) intervention for students with dyslexia who are learning American Sign Language. (ASL). The number of educational institutions offering ASL as a foreign language is on the rise; ASL has become the third most taught language in the U.S. (Mitchell, 2006). However, there is a misconception that it is an easier language to learn than orthographic languages. In fact, ASL is a complex language with its own grammatical rules including complex syntax and semantics. Learning a visual language may present a unique challenge to dyslexic learners; it requires constant visual attention, paired-associate learning and recall, which may be challenging for dyslexic learners. Cover Copy Compare (CCC) as an intervention for dyslexic learners has proven successful in learning other content (i.e., spelling words). CCC strategies were implemented to reinforce ASL vocabulary to four post-secondary students identified with dyslexia. By using CCC in a multimedia format, the need for receptive and expressive skills are addressed, therefore addressing both aspects of ASL acquisition. The major findings suggest participants improved recall of signs as an expressive skill. As they are able to recall signs with greater fluency, their ability to communicate with fluence can increase as well

Speech assessment and characterization for law enforcement applications

Speech signals acquired, transmitted or stored in non-ideal conditions are often degraded by one or more effects including, for example, additive noise. These degradations alter the signal properties in a manner that deteriorates the intelligibility or quality of the speech signal. In the law enforcement context such degradations are commonplace due to the limitations in the audio collection methodology, which is often required to be covert. In severe degradation conditions, the acquired signal may become unintelligible, losing its value in an investigation and in less severe conditions, a loss in signal quality may be encountered, which can lead to higher transcription time and cost. This thesis proposes a non-intrusive speech assessment framework from which algorithms for speech quality and intelligibility assessment are derived, to guide the collection and transcription of law enforcement audio. These methods are trained on a large database labelled using intrusive techniques (whose performance is verified with subjective scores) and shown to perform favorably when compared with existing non-intrusive techniques. Additionally, a non-intrusive CODEC identification and verification algorithm is developed which can identify a CODEC with an accuracy of 96.8 % and detect the presence of a CODEC with an accuracy higher than 97 % in the presence of additive noise. Finally, the speech description taxonomy framework is developed, with the aim of characterizing various aspects of a degraded speech signal, including the mechanism that results in a signal with particular characteristics, the vocabulary that can be used to describe those degradations and the measurable signal properties that can characterize the degradations. The taxonomy is implemented as a relational database that facilitates the modeling of the relationships between various attributes of a signal and promises to be a useful tool for training and guiding audio analysts