Phonetic Event-based Whole-Word Modeling Approaches for Speech Recognition

Speech is composed of basic speech sounds called phonemes, and these subword units are the foundation of most speech recognition systems. While detailed acoustic models of phones (and phone sequences) are common, most recognizers model words themselves as a simple concatenation of phonemes and do not closely model the temporal relationships between phonemes within words. Human speech production is constrained by the movement of speech articulators, and there is abundant evidence to indicate that human speech recognition is inextricably linked to the temporal patterns of speech sounds. Structures such as the hidden Markov model (HMM) have proved extremely useful and effective because they offer a convenient framework for combining acoustic modeling of phones with powerful probabilistic language models. However, this convenience masks deficiencies in temporal modeling. Additionally, robust recognition requires complex automatic speech recognition (ASR) systems and entails non-trivial computational costs. As an alternative, we extend previous work on the point process model (PPM) for keyword spotting, an approach to speech recognition expressly based on whole-word modeling of the temporal relations of phonetic events. In our research, we have investigated and advanced a number of major components of this system. First, we have considered alternate methods of determining phonetic events from phone posteriorgrams. We have introduced several parametric approaches to modeling intra-word phonetic timing distributions which allow us to cope with data sparsity issues. We have substantially improved algorithms used to compute keyword detections, capitalizing on the sparse nature of the phonetic input which permits the system to be scaled to large data sets. We have considered enhanced CART-based modeling of phonetic timing distributions based on related text-to-speech synthesis work. Lastly, we have developed a point process based spoken term detection system and applied it to the conversational telephone speech task of the 2006 NIST Spoken Term Detection evaluation. We demonstrate the PPM system to be competitive with state-of-the-art phonetic search systems while requiring significantly fewer computational resources

Low Resource Efficient Speech Retrieval

Speech retrieval refers to the task of retrieving the information, which is useful or relevant to a user query, from speech collection. This thesis aims to examine ways in which speech retrieval can be improved in terms of requiring low resources - without extensively annotated corpora on which automated processing systems are typically built - and achieving high computational efficiency. This work is focused on two speech retrieval technologies, spoken keyword retrieval and spoken document classification. Firstly, keyword retrieval - also referred to as keyword search (KWS) or spoken term detection - is defined as the task of retrieving the occurrences of a keyword specified by the user in text form, from speech collections. We make advances in an open vocabulary KWS platform using context-dependent Point Process Model (PPM). We further accomplish a PPM-based lattice generation framework, which improves KWS performance and enables automatic speech recognition (ASR) decoding. Secondly, the massive volumes of speech data motivate the effort to organize and search speech collections through spoken document classification. In classifying real-world unstructured speech into predefined classes, the wildly collected speech recordings can be extremely long, of varying length, and contain multiple class label shifts at variable locations in the audio. For this reason each spoken document is often first split into sequential segments, and then each segment is independently classified. We present a general purpose method for classifying spoken segments, using a cascade of language independent acoustic modeling, foreign-language to English translation lexicons, and English-language classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large classification performance improvements. Lastly, we remove the need of any orthographic lexicon and instead exploit alternative unsupervised approaches to decoding speech in terms of automatically discovered word-like or phoneme-like units. We show that the spoken segment representations based on such lexical or phonetic discovery can achieve competitive classification performance as compared to those based on a domain-mismatched ASR or a universal phone set ASR

Features of hearing: applications of machine learning to uncover the building blocks of hearing

Recent advances in machine learning have instigated a renewed interest in using machine learning approaches to better understand human sensory processing. This line of research is particularly interesting for speech research since speech comprehension is uniquely human, which complicates obtaining detailed neural recordings. In this thesis, I explore how machine learning can be used to uncover new knowledge about the auditory system, with a focus on discovering robust auditory features. The resulting increased understanding of the noise robustness of human hearing may help to better assist those with hearing loss and improve Automatic Speech Recognition (ASR) systems. First, I show how computational neuroscience and machine learning can be combined to generate hypotheses about auditory features. I introduce a neural feature detection model with a modest number of parameters that is compatible with auditory physiology. By testing feature detector variants in a speech classification task, I confirm the importance of both well-studied and lesser-known auditory features. Second, I investigate whether ASR software is a good candidate model of the human auditory system. By comparing several state-of-the-art ASR systems to the results from humans on a range of psychometric experiments, I show that these ASR systems diverge markedly from humans in at least some psychometric tests. This implies that none of these systems act as a strong proxy for human speech recognition, although some may be useful when asking more narrowly defined questions. For neuroscientists, this thesis exemplifies how machine learning can be used to generate new hypotheses about human hearing, while also highlighting the caveats of investigating systems that may work fundamentally differently from the human brain. For machine learning engineers, I point to tangible directions for improving ASR systems. To motivate the continued cross-fertilization between these fields, a toolbox that allows researchers to assess new ASR systems has been released.Open Acces

Ultra-high-speed imaging of bubbles interacting with cells and tissue

Ultrasound contrast microbubbles are exploited in molecular imaging, where bubbles are directed to target cells and where their high-scattering cross section to ultrasound allows for the detection of pathologies at a molecular level. In therapeutic applications vibrating bubbles close to cells may alter the permeability of cell membranes, and these systems are therefore highly interesting for drug and gene delivery applications using ultrasound. In a more extreme regime bubbles are driven through shock waves to sonoporate or kill cells through intense stresses or jets following inertial bubble collapse. Here, we elucidate some of the underlying mechanisms using the 25-Mfps camera Brandaris128, resolving the bubble dynamics and its interactions with cells. We quantify acoustic microstreaming around oscillating bubbles close to rigid walls and evaluate the shear stresses on nonadherent cells. In a study on the fluid dynamical interaction of cavitation bubbles with adherent cells, we find that the nonspherical collapse of bubbles is responsible for cell detachment. We also visualized the dynamics of vibrating microbubbles in contact with endothelial cells followed by fluorescent imaging of the transport of propidium iodide, used as a membrane integrity probe, into these cells showing a direct correlation between cell deformation and cell membrane permeability

Audio computing in the wild: frameworks for big data and small computers

This dissertation presents some machine learning algorithms that are designed to process as much data as needed while spending the least possible amount of resources, such as time, energy, and memory. Examples of those applications, but not limited to, can be a large-scale multimedia information retrieval system where both queries and the items in the database are noisy signals; collaborative audio enhancement from hundreds of user-created clips of a music concert; an event detection system running in a small device that has to process various sensor signals in real time; a lightweight custom chipset for speech enhancement on hand-held devices; instant music analysis engine running on smartphone apps. In all those applications, efficient machine learning algorithms are supposed to achieve not only a good performance, but also a great resource-efficiency. We start from some efficient dictionary-based single-channel source separation algorithms. We can train this kind of source-specific dictionaries by using some matrix factorization or topic modeling, whose elements form a representative set of spectra for the particular source. During the test time, the system estimates the contribution of the participating dictionary items for an unknown mixture spectrum. In this way we can estimate the activation of each source separately, and then recover the source of interest by using that particular source's reconstruction. There are some efficiency issues during this procedure. First off, searching for the optimal dictionary size is time consuming. Although for some very common types of sources, e.g. English speech, we know the optimal rank of the model by trial and error, it is hard to know in advance as to what is the optimal number of dictionary elements for the unknown sources, which are usually modeled during the test time in the semi-supervised separation scenarios. On top of that, when it comes to the non-stationary unknown sources, we had better maintain a dictionary that adapts its size and contents to the change of the source's nature. In this online semi-supervised separation scenario, a mechanism that can efficiently learn the optimal rank is helpful. To this end, a deflation method is proposed for modeling this unknown source with a nonnegative dictionary whose size is optimal. Since it has to be done during the test time, the deflation method that incrementally adds up new dictionary items shows better efficiency than a corresponding na\"ive approach where we simply try a bunch of different models. We have another efficiency issue when we are to use a large dictionary for better separation. It has been known that considering the manifold of the training data can help enhance the performance for the separation. This is because of the symptom that the usual manifold-ignorant convex combination models, such as from low-rank matrix decomposition or topic modeling, tend to result in ambiguous regions in the source-specific subspace defined by the dictionary items as the bases. For example, in those ambiguous regions, the original data samples cannot reside. Although some source separation techniques that respect data manifold could increase the performance, they call for more memory and computational resources due to the fact that the models call for larger dictionaries and involve sparse coding during the test time. This limitation led the development of hashing-based encoding of the audio spectra, so that some computationally heavy routines, such as nearest neighbor searches for sparse coding, can be performed in a cheaper bit-wise fashion. Matching audio signals can be challenging as well, especially if the signals are noisy and the matching task involves a big amount of signals. If it is an information retrieval application, for example, the bigger size of the data leads to a longer response time. On top of that, if the signals are defective, we have to perform the enhancement or separation job in the first place before matching, or we might need a matching mechanism that is robust to all those different kinds of artifacts. Likewise, the noisy nature of signals can add an additional complexity to the system. In this dissertation we will also see some compact integer (and eventually binary) representations for those matching systems. One of the possible compact representations would be a hashing-based matching method, where we can employ a particular kind of hash functions to preserve the similarity among original signals in the hash code domain. We will see that a variant of Winner Take All hashing can provide Hamming distance from noise-robust binary features, and that matching using the hash codes works well for some keyword spotting tasks. From the fact that some landmark hashes (e.g. local maxima from non-maximum suppression on the magnitudes of a mel-scaled spectrogram) can also robustly represent the time-frequency domain signal efficiently, a matrix decomposition algorithm is also proposed to take those irregular sparse matrices as input. Based on the assumption that the number of landmarks is a lot smaller than the number of all the time-frequency coefficients, we can think of this matching algorithm efficient if it operates entirely on the landmark representation. On the contrary to the usual landmark matching schemes, where matching is defined rigorously, we see the audio matching problem as soft matching where we find a similar constellation of landmarks to the query. In order to perform this soft matching job, the landmark positions are smoothed by a fixed-width Gaussian caps, with which the matching job is reduced down to calculating the amount of overlaps in-between those Gaussians. The Gaussian-based density approximation is also useful when we perform decomposition on this landmark representation, because otherwise the landmarks are usually too sparse to perform an ordinary matrix factorization algorithm, which are originally for a dense input matrix. We also expand this concept to the matrix deconvolution problem as well, where we see the input landmark representation of a source as a two-dimensional convolution between a source pattern and its corresponding sparse activations. If there are more than one source, as a noisy signal, we can think of this problem as factor deconvolution where the mixture is the combination of all the source-specific convolutions. The dissertation also covers Collaborative Audio Enhancement (CAE) algorithms that aim to recover the dominant source at a sound scene (e.g. music signals of a concert rather than the noise from the crowd) from multiple low-quality recordings (e.g. Youtube video clips uploaded by the audience). CAE can be seen as crowdsourcing a recording job, which needs a substantial amount of denoising effort afterward, because the user-created recordings might have been contaminated with various artifacts. In the sense that the recordings are from not-synchronized heterogenous sensors, we can also think of CAE as big ad-hoc sensor array processing. In CAE, each recording is assumed to be uniquely corrupted by a specific frequency response of the microphone, an aggressive audio coding algorithm, interference, band-pass filtering, clipping, etc. To consolidate all these recordings and come up with an enhanced audio, Probabilistic Latent Component Sharing (PLCS) has been proposed as a method of simultaneous probabilistic topic modeling on synchronized input signals. In PLCS, some of the parameters are fixed to be same during and after the learning process to capture common audio content, while the rest of the parameters are for the unwanted recording-specific interference and artifacts. We can speed up PLCS by incorporating a hashing-based nearest neighbor search so that at every EM iteration PLCS can be applied only to a small number of recordings that are closest to the current source estimation. Experiments on a small simulated CAE setup shows that the proposed PLCS can improve the sound quality from variously contaminated recordings. The nearest neighbor search technique during PLCS provides sensible speed-up at larger scaled experiments (up to 1000 recordings). Finally, to describe an extremely optimized deep learning deployment system, Bitwise Neural Networks (BNN) will be also discussed. In the proposed BNN, all the input, hidden, and output nodes are binaries (+1 and -1), and so are all the weights and bias. Consequently, the operations on them during the test time are defined with Boolean algebra, too. BNNs are spatially and computationally efficient in implementations, since (a) we represent a real-valued sample or parameter with a bit (b) the multiplication and addition correspond to bitwise XNOR and bit-counting, respectively. Therefore, BNNs can be used to implement a deep learning system in a resource-constrained environment, so that we can deploy a deep learning system on small devices without using up the power, memory, CPU clocks, etc. The training procedure for BNNs is based on a straightforward extension of backpropagation, which is characterized by the use of the quantization noise injection scheme, and the initialization strategy that learns a weight-compressed real-valued network only for the initialization purpose. Some preliminary results on the MNIST dataset and speech denoising demonstrate that a straightforward extension of backpropagation can successfully train BNNs whose performance is comparable while necessitating vastly fewer computational resources