OPTIMAL SELECTION OF BITSTREAM FEATURES FOR COMPRESSED-DOMAIN AUTOMATIC SPEAKER RECOGNITION

ABSTRACT Low-complexity compressed-domain automatic speaker recognition algorithms are directly applied to the coded speech bitstream to avoid the computational burden of decoding the parameters and resynthesizing the speech waveform. The objective of this paper is to further reduce the complexity of this approach by determining the smallest set of bitstream features that has the maximum effectiveness on recognition accuracy. For this purpose, recognition accuracy is evaluated with various sets of medium-term statistical features extracted from GSM AMR compressed speech coded at 12.2 kb/s. Over a database of 14 speakers the results show that, using 20 seconds of active speech, a recognition ratio of 100% can be achieved with only nine of the 18 statistical features under analysis. This is a complexity reduction by a factor of two with respect to previous works. Moreover, the robustness of the proposed system has been assessed using test samples of different length and varying levels of frame losses, and proved to be the same of previous approaches

Emotion Recognition from Speech with Acoustic, Non-Linear and Wavelet-based Features Extracted in Different Acoustic Conditions

ABSTRACT: In the last years, there has a great progress in automatic speech recognition. The challenge now it is not only recognize the semantic content in the speech but also the called "paralinguistic" aspects of the speech, including the emotions, and the personality of the speaker. This research work aims in the development of a methodology for the automatic emotion recognition from speech signals in non-controlled noise conditions. For that purpose, different sets of acoustic, non-linear, and wavelet based features are used to characterize emotions in different databases created for such purpose

AMR Compressed-Domain Analysis for Multimedia Forensics Double Compression Detection

An audio recording must be authentic to be admitted as evidence in a criminal prosecution so that the speech is saved with maximum fidelity and interpretation mistakes are prevented. AMR (adaptive multi-rate) encoder is a worldwide standard for speech compression and for GSM mobile network transmission, including 3G and 4G. In addition, such encoder is an audio file format standard with extension AMR which uses the same compression algorithm. Due to its extensive usage in mobile networks and high availability in modern smartphones, AMR format has been found in audio authenticity cases for forgery searching. Such exams compound the multimedia forensics field which consists of, among other techniques, double compression detection, i. e., to determine if a given AMR file was decompressed and compressed again. AMR double compression detection is a complex engineering problem whose solution is still underway. In general terms, if an AMR file is double compressed, it is not an original one and it was likely doctored. The published works in literature about double compression detection are based on decoded waveform AMR files to extract features. In this paper, a new approach is proposed to AMR double compression detection which, in spite of processing decoded audio, uses its encoded version to extract compressed-domain linear prediction (LP) coefficient-based features. By means of feature statistical analysis, it is possible to show that they can be used to achieve AMR double compression detection in an effective way, so that they can be considered a promising path to solve AMR double compression problem by artificial neural networks

Automated Testing of Speech-to-Speech Machine Translation in Telecom Networks

Globalisoituvassa maailmassa kyky kommunikoida kielimuurien yli käy yhä tärkeämmäksi. Kielten opiskelu on työlästä ja siksi halutaan kehittää automaattisia konekäännösjärjestelmiä. Ericsson on kehittänyt prototyypin nimeltä Real-Time Interpretation System (RTIS), joka toimii mobiiliverkossa ja kääntää matkailuun liittyviä fraaseja puhemuodossa kahden kielen välillä. Nykyisten konekäännösjärjestelmien suorituskyky on suhteellisen huono ja siksi testauksella on suuri merkitys järjestelmien suunnittelussa. Testauksen tarkoituksena on varmistaa, että järjestelmä säilyttää käännösekvivalenssin sekä puhekäännösjärjestelmän tapauksessa myös riittävän puheenlaadun. Luotettavimmin testaus voidaan suorittaa ihmisten antamiin arviointeihin perustuen, mutta tällaisen testauksen kustannukset ovat suuria ja tulokset subjektiivisia. Tässä työssä suunniteltiin ja analysoitiin automatisoitu testiympäristö Real-Time Interpretation System -käännösprototyypille. Tavoitteina oli tutkia, voidaanko testaus suorittaa automatisoidusti ja pystytäänkö todellinen, käyttäjän havaitsema käännösten laatu mittaamaan automatisoidun testauksen keinoin. Tulokset osoittavat että mobiiliverkoissa puheenlaadun testaukseen käytetyt menetelmät eivät ole optimaalisesti sovellettavissa konekäännösten testaukseen. Nykytuntemuksen mukaan ihmisten suorittama arviointi on ainoa luotettava tapa mitata käännösekvivalenssia ja puheen ymmärrettävyyttä. Konekäännösten testauksen automatisointi vaatii lisää tutkimusta, jota ennen subjektiivinen arviointi tulisi säilyttää ensisijaisena testausmenetelmänä RTIS-testauksessa.In the globalizing world, the ability to communicate over language barriers is increasingly important. Learning languages is laborious, which is why there is a strong desire to develop automatic machine translation applications. Ericsson has developed a speech-to-speech translation prototype called the Real-Time Interpretation System (RTIS). The service runs in a mobile network and translates travel phrases between two languages in speech format. The state-of-the-art machine translation systems suffer from a relatively poor performance and therefore evaluation plays a big role in machine translation development. The purpose of evaluation is to ensure the system preserves the translational equivalence, and in case of a speech-to-speech system, the speech quality. The evaluation is most reliably done by human judges. However, human-conducted evaluation is costly and subjective. In this thesis, a test environment for Ericsson Real-Time Interpretation System prototype is designed and analyzed. The goals are to investigate if the RTIS verification can be conducted automatically, and if the test environment can truthfully measure the end-to-end performance of the system. The results conclude that methods used in end-to-end speech quality verification in mobile networks can not be optimally adapted for machine translation evaluation. With current knowledge, human-conducted evaluation is the only method that can truthfully measure translational equivalence and the speech intelligibility. Automating machine translation evaluation needs further research, until which human-conducted evaluation should remain the preferred method in RTIS verification

Characterization of speaker recognition in noisy channels

Speaker recognition is a frequently overlooked form of biometric security. Text-independent speaker identification is used by financial services, forensic experts, and human computer interaction developers to extract information that is transmitted along with a spoken message such as identity, gender, age, emotional state, etc. of a speaker. Speech features are classified as either low-level or high-level characteristics. Highlevel speech features are associated with syntax, dialect, and the overall meaning of a spoken message. In contrast, low-level features such as pitch, and phonemic spectra are associated much more with the physiology of the human vocal tract. It is these lowlevel features that are also the easiest and least computationally intensive characteristics of speech to extract. Once extracted, modern speaker recognition systems attempt to fit these features best to statistical classification models. One such widely used model is the Gaussian Mixture Model (GMM). The current standard of testing of speaker recognition systems is standardized by NIST in the often updated NIST Speaker Recognition Evaluation (NIST-SRE) standard. The results measured by the tests outlined in the standard are ultimately presented as Detection Error Tradeoff (DET) curves and detection cost function scores. A new method of measuring the effects of channel impediments on the quality of identifications made by Gaussian Mixture Model based speaker recognition systems will be presented in this thesis. With the exception of the NIST-SRE, no standardized or extensive testing of speaker recognition systems in noisy channels has been conducted. Thorough testing of speaker recognition systems will be conducted in channel model simulators. Additionally, the NIST-SRE error metric will be evaluated against a new proposed metric for gauging the performance and improvements of speaker recognition systems

The Effect Of Acoustic Variability On Automatic Speaker Recognition Systems

This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre-forensic use. The thesis begins with a literature review and explanation of relevant technical terms. Five categories of research experiments then examine ASR performance, reflective of conditions influencing speech quantity (inhibitors) and speech quality (contaminants), acknowledging quality often influences quantity. Experiments pertain to: net speech duration, signal to noise ratio (SNR), reverberation, frequency bandwidth and transcoding (codecs). The ASR system is placed under scrutiny with examination of settings and optimum conditions (e.g. matched/unmatched test audio and speaker models). Output is examined in relation to baseline performance and metrics assist in informing if ASRs should be applied to suboptimal audio recordings. Results indicate that modern ASRs are relatively resilient to low and moderate levels of the acoustic contaminants and inhibitors examined, whilst remaining sensitive to higher levels. The thesis provides discussion on issues such as the complexity and fragility of the speech signal path, speaker variability, difficulty in measuring conditions and mitigation (thresholds and settings). The application of ASRs to casework is discussed with recommendations, acknowledging the different modes of operation (e.g. investigative usage) and current UK limitations regarding presenting ASR output as evidence in criminal trials. In summary, and in the context of acoustic variability, the thesis recommends that ASRs could be applied to pre-forensic cases, accepting extraneous issues endure which require governance such as validation of method (ASR standardisation) and population data selection. However, ASRs remain unsuitable for broad forensic application with many acoustic conditions causing irrecoverable speech data loss contributing to high error rates

Non-intrusive identification of speech codecs in digital audio signals

Speech compression has become an integral component in all modern telecommunications networks. Numerous codecs have been developed and deployed for efficiently transmitting voice signals while maintaining high perceptual quality. Because of the diversity of speech codecs used by different carriers and networks, the ability to distinguish between different codecs lends itself to a wide variety of practical applications, including determining call provenance, enhancing network diagnostic metrics, and improving automated speaker recognition. However, few research efforts have attempted to provide a methodology for identifying amongst speech codecs in an audio signal. In this research, we demonstrate a novel approach for accurately determining the presence of several contemporary speech codecs in a non-intrusive manner. The methodology developed in this research demonstrates techniques for analyzing an audio signal such that the subtle noise components introduced by the codec processing are accentuated while most of the original speech content is eliminated. Using these techniques, an audio signal may be profiled to gather a set of values that effectively characterize the codec present in the signal. This procedure is first applied to a large data set of audio signals from known codecs to develop a set of trained profiles. Thereafter, signals from unknown codecs may be similarly profiled, and the profiles compared to each of the known training profiles in order to decide which codec is the best match with the unknown signal. Overall, the proposed strategy generates extremely favorable results, with codecs being identified correctly in nearly 95% of all test signals. In addition, the profiling process is shown to require a very short analysis length of less than 4 seconds of audio to achieve these results. Both the identification rate and the small analysis window represent dramatic improvements over previous efforts in speech codec identification

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

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity