A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks

A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks

The Phonological Process with Two Patterns of Simplified Chinese Characters

This paper analyzed word recognition in two patterns of Chinese characters, cross referenced with word frequency. The patterns were defined as uni-part (semantic radical/component only) and bi-part (including the phonetic radical/component and the semantic radical/component) characters. The interactions of semantic and phonological access in both patterns were inspected. It was observed that in the naming task and the pronunciation-matching task, the subject performance involving the uni-part characters showed longer RT than the bi-part characters. However, with the lexical decision and meaning-matching tasks the uni-part characters showed shorter RT than the bi-part characters. It was also observed that the frequency, which is regarded as a lexical variable, displayed a strong influence. This suggests that Chinese characters require lexical access in all tasks. This study also suggested that the phonological process is primary in visual word recognition; as there is a significant phonological effect in processing the Chinese bi-part characters, resulting in either the facilitation or inhibition of phonology due to the differing demands of the two task

Segmentation ART: A Neural Network for Word Recognition from Continuous Speech

The Segmentation ATIT (Adaptive Resonance Theory) network for word recognition from a continuous speech stream is introduced. An input sequeuce represents phonemes detected at a preproccesing stage. Segmentation ATIT is trained rapidly, and uses a fast-learning fuzzy ART modules, top-down expectation, and a spatial representation of temporal order. The network performs on-line identification of word boundaries, correcting an initial hypothesis if subsequent phonemes are incompatible with a previous partition. Simulations show that the system's segmentation perfonnance is comparable to that of TRACE, and the ability to segment a number of difficult phrases is also demonstrated.National Science Foundation (NSF-IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-1-0G57

Why pitch sensitivity matters : event-related potential evidence of metric and syntactic violation detection among spanish late learners of german

Event-related potential (ERP) data in monolingual German speakers have shown that sentential metric expectancy violations elicit a biphasic ERP pattern consisting of an anterior negativity and a posterior positivity (P600). This pattern is comparable to that elicited by syntactic violations. However, proficient French late learners of German do not detect violations of metric expectancy in German. They also show qualitatively and quantitatively different ERP responses to metric and syntactic violations. We followed up the questions whether (1) latter evidence results from a potential pitch cue insensitivity in speech segmentation in French speakers, or (2) if the result is founded in rhythmic language differences. Therefore, we tested Spanish late learners of German, as Spanish, contrary to French, uses pitch as a segmentation cue even though the basic segmentation unit is the same in French and Spanish (i.e., the syllable). We report ERP responses showing that Spanish L2 learners are sensitive to syntactic as well as metric violations in German sentences independent of attention to task in a P600 response. Overall, the behavioral performance resembles that of German native speakers. The current data suggest that Spanish L2 learners are able to extract metric units (trochee) in their L2 (German) even though their basic segmentation unit in Spanish is the syllable. In addition Spanish in contrast to French L2 learners of German are sensitive to syntactic violations indicating a tight link between syntactic and metric competence. This finding emphasizes the relevant role of metric cues not only in L2 prosodic but also in syntactic processing