Unsupervised Phoneme and Word Discovery from Multiple Speakers using Double Articulation Analyzer and Neural Network with Parametric Bias

This paper describes a new unsupervised machine learning method for simultaneous phoneme and word discovery from multiple speakers. Human infants can acquire knowledge of phonemes and words from interactions with his/her mother as well as with others surrounding him/her. From a computational perspective, phoneme and word discovery from multiple speakers is a more challenging problem than that from one speaker because the speech signals from different speakers exhibit different acoustic features. This paper proposes an unsupervised phoneme and word discovery method that simultaneously uses nonparametric Bayesian double articulation analyzer (NPB-DAA) and deep sparse autoencoder with parametric bias in hidden layer (DSAE-PBHL). We assume that an infant can recognize and distinguish speakers based on certain other features, e.g., visual face recognition. DSAE-PBHL is aimed to be able to subtract speaker-dependent acoustic features and extract speaker-independent features. An experiment demonstrated that DSAE-PBHL can subtract distributed representations of acoustic signals, enabling extraction based on the types of phonemes rather than on the speakers. Another experiment demonstrated that a combination of NPB-DAA and DSAE-PB outperformed the available methods in phoneme and word discovery tasks involving speech signals with Japanese vowel sequences from multiple speakers.Comment: 21 pages. Submitte

Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery

Infants acquire words and phonemes from unsegmented speech signals using segmentation cues, such as distributional, prosodic, and co-occurrence cues. Many pre-existing computational models that represent the process tend to focus on distributional or prosodic cues. This paper proposes a nonparametric Bayesian probabilistic generative model called the prosodic hierarchical Dirichlet process-hidden language model (Prosodic HDP-HLM). Prosodic HDP-HLM, an extension of HDP-HLM, considers both prosodic and distributional cues within a single integrative generative model. We conducted three experiments on different types of datasets, and demonstrate the validity of the proposed method. The results show that the Prosodic DAA successfully uses prosodic cues and outperforms a method that solely uses distributional cues. The main contributions of this study are as follows: 1) We develop a probabilistic generative model for time series data including prosody that potentially has a double articulation structure; 2) We propose the Prosodic DAA by deriving the inference procedure for Prosodic HDP-HLM and show that Prosodic DAA can discover words directly from continuous human speech signals using statistical information and prosodic information in an unsupervised manner; 3) We show that prosodic cues contribute to word segmentation more in naturally distributed case words, i.e., they follow Zipf's law.Comment: 11 pages, Submitted to IEEE Transactions on Cognitive and Developmental System

SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model

To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand the environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inference easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environments and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, connected modules are dependent on each other, and parameters are required to be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it becomes harder to derive and implement those of a larger scale model. To solve these problems, in this paper, we propose a method for parameter estimation by communicating the minimal parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed

Symbol Emergence in Cognitive Developmental Systems: a Survey

OAPA Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to symbols. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems

Do Infants Really Learn Phonetic Categories?

Early changes in infants’ ability to perceive native and nonnative speech sound contrasts are typically attributed to their developing knowledge of phonetic categories. We critically examine this hypothesis and argue that there is little direct evidence of category knowledge in infancy. We then propose an alternative account in which infants’ perception changes because they are learning a perceptual space that is appropriate to represent speech, without yet carving up that space into phonetic categories. If correct, this new account has substantial implications for understanding early language development

Identifying Speaker State from Multimodal Cues

Automatic identification of speaker state is essential for spoken language understanding, with broad potential in various real-world applications. However, most existing work has focused on recognizing a limited set of emotional states using cues from a single modality. This thesis describes my research that addresses these limitations and challenges associated with speaker state identification by studying a wide range of speaker states, including emotion and sentiment, humor, and charisma, using features from speech, text, and visual modalities. The first part of this thesis focuses on emotion and sentiment recognition in speech. Emotion and sentiment recognition is one of the most studied topics in speaker state identification and has gained increasing attention in speech research recently, with extensive emotional speech models and datasets published every year. However, most work focuses only on recognizing a set of discrete emotions in high-resource languages such as English, while in real-life conversations, emotion is changing continuously and exists in all spoken languages. To address the mismatch, we propose a deep neural network model to recognize continuous emotion by combining inputs from raw waveform signals and spectrograms. Experimental results on two datasets show that the proposed model achieves state-of-the-art results by exploiting both waveforms and spectrograms as input. Due to the higher number of existing textual sentiment models than speech models in low-resource languages, we also propose a method to bootstrap sentiment labels from text transcripts and use these labels to train a sentiment classifier in speech. Utilizing the speaker state information shared across modalities, we extend speech sentiment recognition from high-resource languages to low-resource languages. Moreover, using the natural verse-level alignment in the audio Bibles across different languages, we also explore cross-lingual and cross-modality sentiment transfer. In the second part of the thesis, we focus on recognizing humor, whose expression is related to emotion and sentiment but has very different characteristics. Unlike emotion and sentiment that can be identified by crowdsourced annotators, humorous expressions are highly individualistic and cultural-specific, making it hard to obtain reliable labels. This results in the lack of data annotated for humor, and thus we propose two different methods to automatically and reliably label humor. First, we develop a framework for generating humor labels on videos, by learning from extensive user-generated comments. We collect and analyze 100 videos, building multimodal humor detection models using speech, text, and visual features, which achieves an F1-score of 0.76. In addition to humorous videos, we also develop another framework for generating humor labels on social media posts, by learning from user reactions to Facebook posts. We collect 785K posts with humor and non-humor scores and build models to detect humor with performance comparable to human labelers. The third part of the thesis focuses on charisma, a commonly found but less studied speaker state with unique challenges -- the definition of charisma varies a lot among perceivers, and the perception of charisma also varies with speakers' and perceivers' different demographic backgrounds. To better understand charisma, we conduct the first gender-balanced study of charismatic speech, including speakers and raters from diverse backgrounds. We collect personality and demographic information from the rater as well as their own speech, and examine individual differences in the perception and production of charismatic speech. We also extend the work to politicians' speech by collecting speaker trait ratings on representative speech segments of politicians and study how the genre, gender, and the rater's political stance influence the charisma ratings of the segments

Efficient Learning Machines

Computer scienc

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p