Robust speech recognition with spectrogram factorisation

Communication by speech is intrinsic for humans. Since the breakthrough of mobile devices and wireless communication, digital transmission of speech has become ubiquitous. Similarly distribution and storage of audio and video data has increased rapidly. However, despite being technically capable to record and process audio signals, only a fraction of digital systems and services are actually able to work with spoken input, that is, to operate on the lexical content of speech. One persistent obstacle for practical deployment of automatic speech recognition systems is inadequate robustness against noise and other interferences, which regularly corrupt signals recorded in real-world environments. Speech and diverse noises are both complex signals, which are not trivially separable. Despite decades of research and a multitude of different approaches, the problem has not been solved to a sufficient extent. Especially the mathematically ill-posed problem of separating multiple sources from a single-channel input requires advanced models and algorithms to be solvable. One promising path is using a composite model of long-context atoms to represent a mixture of non-stationary sources based on their spectro-temporal behaviour. Algorithms derived from the family of non-negative matrix factorisations have been applied to such problems to separate and recognise individual sources like speech. This thesis describes a set of tools developed for non-negative modelling of audio spectrograms, especially involving speech and real-world noise sources. An overview is provided to the complete framework starting from model and feature definitions, advancing to factorisation algorithms, and finally describing different routes for separation, enhancement, and recognition tasks. Current issues and their potential solutions are discussed both theoretically and from a practical point of view. The included publications describe factorisation-based recognition systems, which have been evaluated on publicly available speech corpora in order to determine the efficiency of various separation and recognition algorithms. Several variants and system combinations that have been proposed in literature are also discussed. The work covers a broad span of factorisation-based system components, which together aim at providing a practically viable solution to robust processing and recognition of speech in everyday situations

Sparse and Low-rank Modeling for Automatic Speech Recognition

This thesis deals with exploiting the low-dimensional multi-subspace structure of speech towards the goal of improving acoustic modeling for automatic speech recognition (ASR). Leveraging the parsimonious hierarchical nature of speech, we hypothesize that whenever a speech signal is measured in a high-dimensional feature space, the true class information is embedded in low-dimensional subspaces whereas noise is scattered as random high-dimensional erroneous estimations in the features. In this context, the contribution of this thesis is twofold: (i) identify sparse and low-rank modeling approaches as excellent tools for extracting the class-specific low-dimensional subspaces in speech features, and (ii) employ these tools under novel ASR frameworks to enrich the acoustic information present in the speech features towards the goal of improving ASR. Techniques developed in this thesis focus on deep neural network (DNN) based posterior features which, under the sparse and low-rank modeling approaches, unveil the underlying class-specific low-dimensional subspaces very elegantly. In this thesis, we tackle ASR tasks of varying difficulty, ranging from isolated word recognition (IWR) and connected digit recognition (CDR) to large-vocabulary continuous speech recognition (LVCSR). For IWR and CDR, we propose a novel \textit{Compressive Sensing} (CS) perspective towards ASR. Here exemplar-based speech recognition is posed as a problem of recovering sparse high-dimensional word representations from compressed low-dimensional phonetic representations. In the context of LVCSR, this thesis argues that albeit their power in representation learning, DNN based acoustic models still have room for improvement in exploiting the \textit{union of low-dimensional subspaces} structure of speech data. Therefore, this thesis proposes to enhance DNN posteriors by projecting them onto the manifolds of the underlying classes using principal component analysis (PCA) or compressive sensing based dictionaries. Projected posteriors are shown to be more accurate training targets for learning better acoustic models, resulting in improved ASR performance. The proposed approach is evaluated on both close-talk and far-field conditions, confirming the importance of sparse and low-rank modeling of speech in building a robust ASR framework. Finally, the conclusions of this thesis are further consolidated by an information theoretic analysis approach which explicitly quantifies the contribution of proposed techniques in improving ASR

Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines

The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing

WHERE IS THE LOCUS OF DIFFICULTY IN RECOGNIZING FOREIGN-ACCENTED WORDS? NEIGHBORHOOD DENSITY AND PHONOTACTIC PROBABILITY EFFECTS ON THE RECOGNITION OF FOREIGN-ACCENTED WORDS BY NATIVE ENGLISH LISTENERS

This series of experiments (1) examined whether native listeners experience recognition difficulty in all kinds of foreign-accented words or only in a subset of words with certain lexical and sub-lexical characteristics-- neighborhood density and phonotactic probability; (2) identified the locus of foreign-accented word recognition difficulty, and (3) investigated how accent-induced mismatches impact the lexical retrieval process. Experiments 1 and 4 examined the recognition of native-produced and foreign-accented words varying in neighborhood density with auditory lexical decision and perceptual identification tasks respectively, which emphasize the lexical level of processing. Findings from Experiment 1 revealed increased accent-induced processing cost in reaction times, especially for words with many similar sounding words, implying that native listeners increase their reliance on top-down lexical knowledge during foreign-accented word recognition. Analysis of perception errors from Experiment 4 found the misperceptions in the foreign-accented condition to be more similar to the target words than those in the native-produced condition. This suggests that accent-induced mismatches tend to activate similar sounding words as alternative word candidates, which possibly pose increased lexical competition for the target word and result in greater processing costs for foreign-accented word recognition at the lexical level. Experiments 2 and 3 examined the sub-lexical processing of the foreign-accented words varying in neighborhood density and phonotactic probability respectively with a same-different matching task, which emphasizes the sub-lexical level of processing. Findings from both experiments revealed no extra processing costs , in either reaction times or accuracy rates, for the foreign-accented stimuli, implying that the sub-lexical processing of the foreign-accented words is as good as that of the native-produced words. Taken together, the overall recognition difficulty of foreign-accented stimuli, as well as the differentially increased processing difficulty for accented dense words (observed in Experiment 1), mainly stems from the lexical level, due to the increased lexical competition posed by the similar sounding word candidates

The effects of high variability training on voice identity learning.

High variability training has been shown to benefit the learning of new face identities. In three experiments, we investigated whether this is also the case for voice identity learning. In Experiment 1a, we contrasted high variability training sets - which included stimuli extracted from a number of different recording sessions, speaking environments and speaking styles - with low variability stimulus sets that only included a single speaking style (read speech) extracted from one recording session (see Ritchie & Burton, 2017 for faces). Listeners were tested on an old/new recognition task using read sentences (i.e. test materials fully overlapped with the low variability training stimuli) and we found a high variability disadvantage. In Experiment 1b, listeners were trained in a similar way, however, now there was no overlap in speaking style or recording session between training sets and test stimuli. Here, we found a high variability advantage. In Experiment 2, variability was manipulated in terms of the number of unique items as opposed to number of unique speaking styles. Here, we contrasted the high variability training sets used in Experiment 1a with low variability training sets that included the same breadth of styles, but fewer unique items; instead, individual items were repeated (see Murphy, Ipser, Gaigg, & Cook, 2015 for faces). We found only weak evidence for a high variability advantage, which could be explained by stimulus-specific effects. We propose that high variability advantages may be particularly pronounced when listeners are required to generalise from trained stimuli to different-sounding, previously unheard stimuli. We discuss these findings in the context of mechanisms thought to underpin advantages for high variability training

Probing Real Sensory Worlds of Receivers with Unsupervised Clustering

The task of an organism to extract information about the external environment from sensory signals is based entirely on the analysis of ongoing afferent spike activity provided by the sense organs. We investigate the processing of auditory stimuli by an acoustic interneuron of insects. In contrast to most previous work we do this by using stimuli and neurophysiological recordings directly in the nocturnal tropical rainforest, where the insect communicates. Different from typical recordings in sound proof laboratories, strong environmental noise from multiple sound sources interferes with the perception of acoustic signals in these realistic scenarios. We apply a recently developed unsupervised machine learning algorithm based on probabilistic inference to find frequently occurring firing patterns in the response of the acoustic interneuron. We can thus ask how much information the central nervous system of the receiver can extract from bursts without ever being told which type and which variants of bursts are characteristic for particular stimuli. Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab. Simultaneous recordings in two preparations exposed to the same acoustic environment reveal that characteristics of burst patterns are largely preserved among individuals of the same species. Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions. This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands

Efficient and Robust Methods for Audio and Video Signal Analysis

This thesis presents my research concerning audio and video signal processing and machine learning. Specifically, the topics of my research include computationally efficient classifier compounds, automatic speech recognition (ASR), music dereverberation, video cut point detection and video classification.Computational efficacy of information retrieval based on multiple measurement modalities has been considered in this thesis. Specifically, a cascade processing framework, including a training algorithm to set its parameters has been developed for combining multiple detectors or binary classifiers in computationally efficient way. The developed cascade processing framework has been applied on video information retrieval tasks of video cut point detection and video classification. The results in video classification, compared to others found in the literature, indicate that the developed framework is capable of both accurate and computationally efficient classification. The idea of cascade processing has been additionally adapted for the ASR task. A procedure for combining multiple speech state likelihood estimation methods within an ASR framework in cascaded manner has been developed. The results obtained clearly show that without impairing the transcription accuracy the computational load of ASR can be reduced using the cascaded speech state likelihood estimation process.Additionally, this thesis presents my work on noise robustness of ASR using a nonnegative matrix factorization (NMF) -based approach. Specifically, methods for transformation of sparse NMF-features into speech state likelihoods has been explored. The results reveal that learned transformations from NMF activations to speech state likelihoods provide better ASR transcription accuracy than dictionary label -based transformations. The results, compared to others in a noisy speech recognition -challenge show that NMF-based processing is an efficient strategy for noise robustness in ASR.The thesis also presents my work on audio signal enhancement, specifically, on removing the detrimental effect of reverberation from music audio. In the work, a linear prediction -based dereverberation algorithm, which has originally been developed for speech signal enhancement, was applied for music. The results obtained show that the algorithm performs well in conjunction with music signals and indicate that dynamic compression of music does not impair the dereverberation performance