Search for: All records

The manner in which acoustic features contribute to perceiving speaker identity remains unclear. In an attempt to better understand speaker perception, we investigated human and machine speaker discrimination with utterances shorter than 2 seconds. Sixty-five listeners performed a same vs. different task. Machine performance was estimated with i-vector/PLDA-based automatic speaker verification systems, one using mel-frequency cepstral coefficients (MFCCs) and the other using voice quality features (VQual2) inspired by a psychoacoustic model of voice quality. Machine performance was measured in terms of the detection and log-likelihood-ratio cost functions. Humans showed higher confidence for correct target decisions compared to correct non-target decisions, suggesting that they rely on different features and/or decision making strategies when identifying a single speaker compared to when distinguishing between speakers. For non-target trials, responses were highly correlated between humans and the VQual2-based system, especially when speakers were perceptually marked. Fusing human responses with an MFCC-based system improved performance over human-only or MFCC-only results, while fusing with the VQual2-based system did not. The study is a step towards understanding human speaker discrimination strategies and suggests that automatic systems might be able to supplement human decisions especially when speakers are marked. 

Automatic speech recognition (ASR) systems for children have lagged behind in performance when compared to adult ASR. The exact problems and evaluation methods for child ASR have not yet been fully investigated. Recent work from the robotics community suggests that ASR for kindergarten speech is especially difficult, even though this age group may benefit most from voice-based educational and diagnostic tools. Our study focused on ASR performance for specific grade levels (K-10) using a word identification task. Grade-specific ASR systems were evaluated, with particular attention placed on the evaluation of kindergarten-aged children (5-6 years old). Experiments included investigation of grade-specific interactions with triphone models using feature space maximum likelihood linear regression (fMLLR), vocal tract length normalization (VTLN), and subglottal resonance (SGR) normalization. Our results indicate that kindergarten ASR performs dramatically worse than even 1st grade ASR, likely due to large speech variability at that age. As such, ASR systems may require targeted evaluations on kindergarten speech rather than being evaluated under the guise of “child ASR.” Additionally, results show that systems trained in matched conditions on kindergarten speech may be less suitable than mismatched-grade training with 1st grade speech. Finally, we analyzed the phonetic errors made by the kindergarten ASR. 

This pilot study investigated the feasibility of implementing child-friendly robots for administering clinical and educational assessments with young children. JIBO, a social robot, was used as a new interface to administer a letter and number naming task and the 3rd Goldman Fristoe Test of Articulation (GFTA-3). The reason for using these assessment materials is to develop robust automatic speech recognition (ASR) and automated social interaction systems that can aid in administering such assessments more efficiently. The voice of JIBO simulates interaction with a peer, and images and playful transitions are displayed on JIBO’s face/screen. Several preliminary observations with 15 pre-kindergarten and 18 kindergarten students included the rate of task completion and strategies to increase student participation. Changes to the length and prompt delivery of the assessment protocol were considered based on these observations, and further observations are planned for future work with an additional cohort of 43 prekindergarten and 50 kindergarten students. Recommendations are given to inform future implementations and analyses.