A discrete wavelet transform-based voice activity detection and noise classification with sub-band selection

A real-time discrete wavelet transform-based adaptive voice activity detector and sub-band selection for feature extraction are proposed for noise classification, which can be used in a speech processing pipeline. The voice activity detection and sub-band selection rely on wavelet energy features and the feature extraction process involves the extraction of mel-frequency cepstral coefficients from selected wavelet sub-bands and mean absolute values of all sub-bands. The method combined with a feedforward neural network with two hidden layers could be added to speech enhancement systems and deployed in hearing devices such as cochlear implants. In comparison to the conventional short-time Fourier transform-based technique, it has higher F1 scores and classification accuracies (with a mean of 0.916 and 90.1%, respectively) across five different noise types (babble, factory, pink, Volvo (car) and white noise), a significantly smaller feature set with 21 features, reduced memory requirement, faster training convergence and about half the computational cost

‘Did the speaker change?’: Temporal tracking for overlapping speaker segmentation in multi-speaker scenarios

Diarization systems are an essential part of many speech processing applications, such as speaker indexing, improving automatic speech recognition (ASR) performance and making single speaker-based algorithms available for use in multi-speaker domains. This thesis will focus on the first task of the diarization process, that being the task of speaker segmentation which can be thought of as trying to answer the question ‘Did the speaker change?’ in an audio recording. This thesis starts by showing that time-varying pitch properties can be used advantageously within the segmentation step of a multi-talker diarization system. It is then highlighted that an individual’s pitch is smoothly varying and, therefore, can be predicted by means of a Kalman filter. Subsequently, it is shown that if the pitch is not predictable, then this is most likely due to a change in the speaker. Finally, a novel system is proposed that uses this approach of pitch prediction for speaker change detection. This thesis then goes on to demonstrate how voiced harmonics can be useful in detecting when more than one speaker is talking, such as during overlapping speaker activity. A novel system is proposed to track multiple harmonics simultaneously, allowing for the determination of onsets and end-points of a speaker’s utterance in the presence of an additional active speaker. This thesis then extends this work to explore the use of a new multimodal approach for overlapping speaker segmentation that tracks both the fundamental frequency (F0) and direction of arrival (DoA) of each speaker simultaneously. The proposed multiple hypothesis tracking system, which simultaneously tracks both features, shows an improvement in segmentation performance when compared to tracking these features separately. Lastly, this thesis focuses on the DoA estimation part of the newly proposed multimodal approach. It does this by exploring a polynomial extension to the multiple signal classification (MUSIC) algorithm, spatio-spectral polynomial (SSP)-MUSIC, and evaluating its performance when using speech sound sources.Open Acces

Model-Based Speech Enhancement

Abstract A method of speech enhancement is developed that reconstructs clean speech from a set of acoustic features using a harmonic plus noise model of speech. This is a significant departure from traditional filtering-based methods of speech enhancement. A major challenge with this approach is to estimate accurately the acoustic features (voicing, fundamental frequency, spectral envelope and phase) from noisy speech. This is achieved using maximum a-posteriori (MAP) estimation methods that operate on the noisy speech. In each case a prior model of the relationship between the noisy speech features and the estimated acoustic feature is required. These models are approximated using speaker-independent GMMs of the clean speech features that are adapted to speaker-dependent models using MAP adaptation and for noise using the Unscented Transform. Objective results are presented to optimise the proposed system and a set of subjective tests compare the approach with traditional enhancement methods. Threeway listening tests examining signal quality, background noise intrusiveness and overall quality show the proposed system to be highly robust to noise, performing significantly better than conventional methods of enhancement in terms of background noise intrusiveness. However, the proposed method is shown to reduce signal quality, with overall quality measured to be roughly equivalent to that of the Wiener filter

ROBUST SPEAKER RECOGNITION BASED ON LATENT VARIABLE MODELS

Automatic speaker recognition in uncontrolled environments is a very challenging task due to channel distortions, additive noise and reverberation. To address these issues, this thesis studies probabilistic latent variable models of short-term spectral information that leverage large amounts of data to achieve robustness in challenging conditions. Current speaker recognition systems represent an entire speech utterance as a single point in a high-dimensional space. This representation is known as "supervector". This thesis starts by analyzing the properties of this representation. A novel visualization procedure of supervectors is presented by which qualitative insight about the information being captured is obtained. We then propose the use of an overcomplete dictionary to explicitly decompose a supervector into a speaker-specific component and an undesired variability component. An algorithm to learn the dictionary from a large collection of data is discussed and analyzed. A subset of the entries of the dictionary is learned to represent speaker-specific information and another subset to represent distortions. After encoding the supervector as a linear combination of the dictionary entries, the undesired variability is removed by discarding the contribution of the distortion components. This paradigm is closely related to the previously proposed paradigm of Joint Factor Analysis modeling of supervectors. We establish a connection between the two approaches and show how our proposed method provides improvements in terms of computation and recognition accuracy. An alternative way to handle undesired variability in supervector representations is to first project them into a lower dimensional space and then to model them in the reduced subspace. This low-dimensional projection is known as "i-vector". Unfortunately, i-vectors exhibit non-Gaussian behavior, and direct statistical modeling requires the use of heavy-tailed distributions for optimal performance. These approaches lack closed-form solutions, and therefore are hard to analyze. Moreover, they do not scale well to large datasets. Instead of directly modeling i-vectors, we propose to first apply a non-linear transformation and then use a linear-Gaussian model. We present two alternative transformations and show experimentally that the transformed i-vectors can be optimally modeled by a simple linear-Gaussian model (factor analysis). We evaluate our method on a benchmark dataset with a large amount of channel variability and show that the results compare favorably against the competitors. Also, our approach has closed-form solutions and scales gracefully to large datasets. Finally, a multi-classifier architecture trained on a multicondition fashion is proposed to address the problem of speaker recognition in the presence of additive noise. A large number of experiments are conducted to analyze the proposed architecture and to obtain guidelines for optimal performance in noisy environments. Overall, it is shown that multicondition training of multi-classifier architectures not only produces great robustness in the anticipated conditions, but also generalizes well to unseen conditions

Machine Learning for Human Activity Detection in Smart Homes

Recognizing human activities in domestic environments from audio and active power consumption sensors is a challenging task since on the one hand, environmental sound signals are multi-source, heterogeneous, and varying in time and on the other hand, the active power consumption varies significantly for similar type electrical appliances. Many systems have been proposed to process environmental sound signals for event detection in ambient assisted living applications. Typically, these systems use feature extraction, selection, and classification. However, despite major advances, several important questions remain unanswered, especially in real-world settings. A part of this thesis contributes to the body of knowledge in the field by addressing the following problems for ambient sounds recorded in various real-world kitchen environments: 1) which features, and which classifiers are most suitable in the presence of background noise? 2) what is the effect of signal duration on recognition accuracy? 3) how do the SNR and the distance between the microphone and the audio source affect the recognition accuracy in an environment in which the system was not trained? We show that for systems that use traditional classifiers, it is beneficial to combine gammatone frequency cepstral coefficients and discrete wavelet transform coefficients and to use a gradient boosting classifier. For systems based on deep learning, we consider 1D and 2D CNN using mel-spectrogram energies and mel-spectrograms images, as inputs, respectively and show that the 2D CNN outperforms the 1D CNN. We obtained competitive classification results for two such systems and validated the performance of our algorithms on public datasets (Google Brain/TensorFlow Speech Recognition Challenge and the 2017 Detection and Classification of Acoustic Scenes and Events Challenge). Regarding the problem of the energy-based human activity recognition in a household environment, machine learning techniques to infer the state of household appliances from their energy consumption data are applied and rule-based scenarios that exploit these states to detect human activity are used. Since most activities within a house are related with the operation of an electrical appliance, this unimodal approach has a significant advantage using inexpensive smart plugs and smart meters for each appliance. This part of the thesis proposes the use of unobtrusive and easy-install tools (smart plugs) for data collection and a decision engine that combines energy signal classification using dominant classifiers (compared in advanced with grid search) and a probabilistic measure for appliance usage. It helps preserving the privacy of the resident, since all the activities are stored in a local database. DNNs received great research interest in the field of computer vision. In this thesis we adapted different architectures for the problem of human activity recognition. We analyze the quality of the extracted features, and more specifically how model architectures and parameters affect the ability of the automatically extracted features from DNNs to separate activity classes in the final feature space. Additionally, the architectures that we applied for our main problem were also applied to text classification in which we consider the input text as an image and apply 2D CNNs to learn the local and global semantics of the sentences from the variations of the visual patterns of words. This work helps as a first step of creating a dialogue agent that would not require any natural language preprocessing. Finally, since in many domestic environments human speech is present with other environmental sounds, we developed a Convolutional Recurrent Neural Network, to separate the sound sources and applied novel post-processing filters, in order to have an end-to-end noise robust system. Our algorithm ranked first in the Apollo-11 Fearless Steps Challenge.Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 676157, project ACROSSIN

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

Comparison for Improvements of Singing Voice Detection System Based on Vocal Separation

Singing voice detection is the task to identify the frames which contain the singer vocal or not. It has been one of the main components in music information retrieval (MIR), which can be applicable to melody extraction, artist recognition, and music discovery in popular music. Although there are several methods which have been proposed, a more robust and more complete system is desired to improve the detection performance. In this paper, our motivation is to provide an extensive comparison in different stages of singing voice detection. Based on the analysis a novel method was proposed to build a more efficiently singing voice detection system. In the proposed system, there are main three parts. The first is a pre-process of singing voice separation to extract the vocal without the music. The improvements of several singing voice separation methods were compared to decide the best one which is integrated to singing voice detection system. And the second is a deep neural network based classifier to identify the given frames. Different deep models for classification were also compared. The last one is a post-process to filter out the anomaly frame on the prediction result of the classifier. The median filter and Hidden Markov Model (HMM) based filter as the post process were compared. Through the step by step module extension, the different methods were compared and analyzed. Finally, classification performance on two public datasets indicates that the proposed approach which based on the Long-term Recurrent Convolutional Networks (LRCN) model is a promising alternative.Comment: 15 page

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo