Some Commonly Used Speech Feature Extraction Algorithms

Speech is a complex naturally acquired human motor ability. It is characterized in adults with the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards. Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively minimized data rate for subsequent processing and analysis. Therefore, acceptable classification is derived from excellent and quality features. Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), Line Spectral Frequencies (LSF), Discrete Wavelet Transform (DWT) and Perceptual Linear Prediction (PLP) are the speech feature extraction techniques that were discussed in these chapter. These methods have been tested in a wide variety of applications, giving them high level of reliability and acceptability. Researchers have made several modifications to the above discussed techniques to make them less susceptible to noise, more robust and consume less time. In conclusion, none of the methods is superior to the other, the area of application would determine which method to select

Percepcijska utemeljenost kepstranih mjera udaljenosti za primjene u obradi govora

Currently, one of the most widely used distance measures in speech and speaker recognition is the Euclidean distance between mel frequency cepstral coefficients (MFCC). MFCCs are based on filter bank algorithm whose filters are equally spaced on a perceptually motivated mel frequency scale. The value of mel cepstral vector, as well as the properties of the corresponding cepstral distance, are determined by several parameters used in mel cepstral analysis. The aim of this work is to examine compatibility of MFCC measure with human perception for different values of parameters in the analysis. By analysing mel filter bank parameters it is found that filter bank with 24 bands, 220 mels bandwidth and band overlap coefficient equal and higher than one gives optimal spectral distortion (SD) distance measures. For this kind of mel filter bank, the difference between vowels can be recognised for full-length mel cepstral SD RMS measure higher than 0.4 - 0.5 dB. Further on, we will show that usage of truncated mel cepstral vector (12 coefficients) is justified for speech recognition, but may be arguable for speaker recognition. We also analysed the impact of aliasing in cepstral domain on cepstral distortion measures. The results showed high correlation of SD distances calculated from aperiodic and periodic mel cepstrum, leading to the conclusion that the impact of aliasing is generally minor. There are rare exceptions where aliasing is present, and these were also analysed.Jedna od danas najčešće korištenih mjera u automatskom prepoznavanju govora i govornika je mjera euklidske udaljenosti MFCC vektora. Algoritam za izračunavanje mel frekvencijskih kepstralnih koeficijenata zasniva se na filtarskom slogu kod kojeg su pojasi ekvidistantno raspoređeni na percepcijski motiviranoj mel skali. Na vrijednost mel kepstralnog vektora, a samim time i na svojstva kepstralne mjere udaljenosti glasova, utječe veći broj parametara sustava za kepstralnu analizu. Tema ovog rada je ispitati usklađenost MFCC mjere sa stvarnim percepcijskim razlikama za različite vrijednosti parametara analize. Analizom parametara mel filtarskog sloga utvrdili smo da filtar sa 24 pojasa, širine 220 mel-a i faktorom preklapanja filtra većim ili jednakim jedan, daje optimalne SD mjere koje se najbolje slažu s percepcijom. Za takav mel filtarski slog granica čujnosti razlike između glasova je 0.4-0.5 dB, mjereno SD RMS razlikom potpunih mel kepstralnih vektora. Također, pokazat ćemo da je korištenje mel kepstralnog vektora odrezanog na konačnu dužinu (12 koeficijenata) opravdano za prepoznavanje govora, ali da bi moglo biti upitno u primjenama prepoznavanja govornika. Analizirali smo i utjecaj preklapanja spektara u kepstralnoj domeni na mjere udaljenosti glasova. Utvrđena je izrazita koreliranost SD razlika izračunatih iz aperiodskog i periodičkog mel kepstra iz čega zaključujemo da je utjecaj preklapanja spektara generalno zanemariv. Postoje rijetke iznimke kod kojih je utjecaj preklapanja spektara prisutan, te su one posebno analizirane

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

Analysis of two adjacent articulation Quranic letters based on MFCC and DTW

—Reciting al-Quran in the correct way is an obligatory duty for Muslims, and therefore learning al-Quran is a continuous education until the correct recitation is achieved. It is important to learn Tajweed rules to master the recitation of Quranic verses. Moreover, mastering the pronunciation of Arabic sounds is the first and key step to achieve accurate recitation of al-Quran. The rules were guided by the Islamic Scholars in fields related to al-Quran from their knowledge and experiences. Very limited researches were found in the perspective of sciences and engineering. In this paper two Quranic letters (ذ and ز) that are articulated from adjacent points of articulation were analyzed using Mel- frequency coefficient analysis. MFCCs matrices were calculated then compared using the dynamic time warping DTW technique to calculate the similarity matrices and find the similarity distance. Results show that letters from the same point of articulation have less similarity distance compared to the letters from different point of articulation

A survey on the feasibility of sound classification on wireless sensor nodes

Wireless sensor networks are suitable to gain context awareness for indoor environments. As sound waves form a rich source of context information, equipping the nodes with microphones can be of great benefit. The algorithms to extract features from sound waves are often highly computationally intensive. This can be problematic as wireless nodes are usually restricted in resources. In order to be able to make a proper decision about which features to use, we survey how sound is used in the literature for global sound classification, age and gender classification, emotion recognition, person verification and identification and indoor and outdoor environmental sound classification. The results of the surveyed algorithms are compared with respect to accuracy and computational load. The accuracies are taken from the surveyed papers; the computational loads are determined by benchmarking the algorithms on an actual sensor node. We conclude that for indoor context awareness, the low-cost algorithms for feature extraction perform equally well as the more computationally-intensive variants. As the feature extraction still requires a large amount of processing time, we present four possible strategies to deal with this problem

An ongoing review of speech emotion recognition

User emotional status recognition is becoming a key feature in advanced Human Computer Interfaces (HCI). A key source of emotional information is the spoken expression, which may be part of the interaction between the human and the machine. Speech emotion recognition (SER) is a very active area of research that involves the application of current machine learning and neural networks tools. This ongoing review covers recent and classical approaches to SER reported in the literature.This work has been carried out with the support of project PID2020-116346GB-I00 funded by the Spanish MICIN

Intelligibility enhancement of synthetic speech in noise

EC Seventh Framework Programme (FP7/2007-2013)Speech technology can facilitate human-machine interaction and create new communication interfaces. Text-To-Speech (TTS) systems provide speech output for dialogue, notification and reading applications as well as personalized voices for people that have lost the use of their own. TTS systems are built to produce synthetic voices that should sound as natural, expressive and intelligible as possible and if necessary be similar to a particular speaker. Although naturalness is an important requirement, providing the correct information in adverse conditions can be crucial to certain applications. Speech that adapts or reacts to different listening conditions can in turn be more expressive and natural. In this work we focus on enhancing the intelligibility of TTS voices in additive noise. For that we adopt the statistical parametric paradigm for TTS in the shape of a hidden Markov model (HMM-) based speech synthesis system that allows for flexible enhancement strategies. Little is known about which human speech production mechanisms actually increase intelligibility in noise and how the choice of mechanism relates to noise type, so we approached the problem from another perspective: using mathematical models for hearing speech in noise. To find which models are better at predicting intelligibility of TTS in noise we performed listening evaluations to collect subjective intelligibility scores which we then compared to the models’ predictions. In these evaluations we observed that modifications performed on the spectral envelope of speech can increase intelligibility significantly, particularly if the strength of the modification depends on the noise and its level. We used these findings to inform the decision of which of the models to use when automatically modifying the spectral envelope of the speech according to the noise. We devised two methods, both involving cepstral coefficient modifications. The first was applied during extraction while training the acoustic models and the other when generating a voice using pre-trained TTS models. The latter has the advantage of being able to address fluctuating noise. To increase intelligibility of synthetic speech at generation time we proposed a method for Mel cepstral coefficient modification based on the glimpse proportion measure, the most promising of the models of speech intelligibility that we evaluated. An extensive series of listening experiments demonstrated that this method brings significant intelligibility gains to TTS voices while not requiring additional recordings of clear or Lombard speech. To further improve intelligibility we combined our method with noise-independent enhancement approaches based on the acoustics of highly intelligible speech. This combined solution was as effective for stationary noise as for the challenging competing speaker scenario, obtaining up to 4dB of equivalent intensity gain. Finally, we proposed an extension to the speech enhancement paradigm to account for not only energetic masking of signals but also for linguistic confusability of words in sentences. We found that word level confusability, a challenging value to predict, can be used as an additional prior to increase intelligibility even for simple enhancement methods like energy reallocation between words. These findings motivate further research into solutions that can tackle the effect of energetic masking on the auditory system as well as on higher levels of processing

Klasifikace emocí v lidské řeči

Import 14/02/2017Dissertation thesis deals with recognition of the emotional state from human speech. The dissertation describes the current state of the Speech Emotion Recognition topic, deals with methods for speech features extraction, classification methods and is devoted to the design of a new system for speech emotion recognition. This system is modeled on the newly created emotional database emoDBova and the new database for stress detection 112DB. Designed speech emotion recognition system is implemented in secure communication infrastructure. The new databases are composed of spontaneous speech in the Czech language. The system for speech emotion recognition is designed on the basis of the last knowledge and to achieve higher accuracy than relevant proposals. The system is implemented to infrastructure, and its role is speech emotion recognition of phone call participants. Above mentioned newly created databases, a unique system for speech emotion recognition and its actual implementation in communications infrastructure are also major contributions of this work.Dizertačná práca sa zaoberá problematikou rozpoznania emočného stavu z reči človeka. Práca popisuje súčasný stav problematiky Speech Emotion Recognition, zaoberá sa metódami na extrakciu rečových príznakov, klasifikačnými metódami a je venovaná návrhu nového systému pre klasifikáciu emočného stavu z reči. Tento systém je namodelovaný na novovytvorenej emočnej databáze emoDBova a databáze pre detekciu stresu 112DB a implementovaný do infraštruktúry zabezpečeného komunikačného systému. Nové databázy sú vytvorené z spontánnej reči v českom jazyku. Systém pre rozpoznávanie emočného stavu je navrhnutý na základe posledných poznatkov a za účelom dosiahnutia vyššej presnosti ako prezentujú doterajšie návrhy. Celý systém je implementovaný do spomínanej infraštruktúry za účelom rozpoznávania emočného stavu účastníkov telefónneho rozhovoru. Spomínané novovytvorené databázy, unikátny systém pre rozpoznanie emočného stavu a jeho reálne nasadenie v komunikačnej infraštruktúre sú hlavnými prínosmi tejto práce.440 - Katedra telekomunikační technikyvyhově

Evaluating automatic speaker recognition systems: an overview of the nist speaker recognition evaluations (1996-2014)

2014 CSIC. Manuscripts published in this Journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.Automatic Speaker Recognition systems show interesting properties, such as speed of processing or repeatability of results, in contrast to speaker recognition by humans. But they will be usable just if they are reliable. Testability, or the ability to extensively evaluate the goodness of the speaker detector decisions, becomes then critical. In the last 20 years, the US National Institute of Standards and Technology (NIST) has organized, providing the proper speech data and evaluation protocols, a series of text-independent Speaker Recognition Evaluations (SRE). Those evaluations have become not just a periodical benchmark test, but also a meeting point of a collaborative community of scientists that have been deeply involved in the cycle of evaluations, allowing tremendous progress in a specially complex task where the speaker information is spread across different information levels (acoustic, prosodic, linguistic…) and is strongly affected by speaker intrinsic and extrinsic variability factors. In this paper, we outline how the evaluations progressively challenged the technology including new speaking conditions and sources of variability, and how the scientific community gave answers to those demands. Finally, NIST SREs will be shown to be not free of inconveniences, and future challenges to speaker recognition assessment will also be discussed