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1. End-to-end Child-Adult Speech Diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model

   Kothalkar, P.V. ( May 2023 , The Journal of the Acoustical Society of America (submitted))

   Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs. controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates an alternate Deep Learning-based diarization solution for segmenting classroom interactions of 3-5 year old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our proposed ResNet model achieves a best F1-score of ∼78.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with Automatic Speech Recognition-based resegmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs. child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations. 

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2. UNDERSTANDING SPEAKER ROLES ON A NATURALISTIC CORPUS: FEARLESS STEPS APOLLO 11

   Chandra-Shekar, M.M. ; Hansen, J.H.L. ( February 2023 , NASA Human Research Program Investigators Conference)

   INTRODUCTION: Apollo-11 (A-11) was the first manned space mission to successfully bring astronauts to the moon and return them safely. Effective team based communications is required for mission specialists to work collaboratively to learn, engage, and solve complex problems. As part of NASA’s goal in assessing team and mission success, all vital speech communications between these personnel were recorded using the multi-track SoundScriber system onto analog tapes, preserving their contribution in the success of one of the greatest achievements in human history. More than +400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in +9k hours of data for A-11. To ensure success of this mission, it was necessary for teams to communicate, learn, and address problems in a timely manner. Previous research has found that compatibility of individual personalities within teams is important for effective team collaboration of those individuals. Hence, it is essential to identify each speaker’s role during an Apollo mission and analyze group communications for knowledge exchange and problem solving to achieve a common goal. Assessing and analyzing speaker roles during the mission can allow for exploring engagement analysis for multi-party speaker situations. METHOD: The UTDallas Fearless steps Apollo data is comprised of 19,000 hours (A-11,A-13,A-1) possessing unique and multiple challenges as it is characterized by severe noise and degradation as well as overlap instances over the 30 channels. For our study, we have selected a subset of 100 hours manually transcribed by professional annotators for speaker labels. The 100 hours are obtained from three mission critical events: 1. Lift-Off (25 hours) 2. Lunar-Landing (50 hours) 3. Lunar-Walking (25 hours). Five channels of interest, out of 30 channels were selected with the most speech activity, the primary speakers operating these five channels are command/owners of these channels. For our analysis, we select five speaker roles: Flight Director (FD), Capsule Communicator (CAPCOM), Guidance, Navigation and, Control (GNC), Electrical, environmental, and consumables manager (EECOM), and Network (NTWK). To track and tag individual speakers across our Fearless Steps audio dataset, we use the concept of ‘where’s Waldo’ to identify all instances of our speakers-of-interest across a cluster of other speakers. Also, to understand speaker roles of our speaker-of-interests, we use speaker duration of primary speaker vs secondary speaker and speaker turns as our metrics to determine the role of the speaker and to understand their responsibility during the three critical phases of the mission. This enables a content linking capability as well as provide a pathway to analyzing group engagement, group dynamics of people working together in an enclosed space, psychological effects, and cognitive analysis in such individuals. IMPACT: NASA’s Apollo Program stands as one of the most significant contributions to humankind. This collection opens new research options for recognizing team communication, group dynamics, and human engagement/psychology for future deep space missions. Analyzing team communications to achieve such goals would allow for the formulation of educational and training technologies for assessment of STEM knowledge, task learning, and educational feedback. Also, identifying these personnel can help pay tribute and yield personal recognition to the hundreds of notable engineers and scientist who made this feat possible. ILLUSTRATION: In this work, we propose to illustrate how a pre-trained speech/language network can be used to obtain powerful speaker embeddings needed for speaker diarization. This framework is used to build these learned embeddings to label unique speakers over sustained audio streams. To train and test our system, we will make use of Fearless Steps Apollo corpus, allowing us to effectively leverage a limited label information resource (100 hours of labeled data out of +9000 hours). Furthermore, we use the concept of 'Finding Waldo' to identify key speakers of interest (SOI) throughout the Apollo-11 mission audio across multiple channel audio streams. 

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3. A Comparison of Speaker-based and Utterance-based Data Selection for Text-to-Speech Synthesis

   https://doi.org/DOI: 10.21437/Interspeech.2018-1313  

   Kai-Zhan Lee, Erica Cooper ( January 2018 , Interspeech 2018)

   Building on previous work in subset selection of training data for text-to-speech (TTS), this work compares speaker-level and utterance-level selection of TTS training data, using acoustic features to guide selection. We find that speaker-based selection is more effective than utterance-based selection, regardless of whether selection is guided by a single feature or a combination of features. We use US English telephone data collected for automatic speech recognition to simulate the conditions of TTS training on low-resource languages. Our best voice achieves a human-evaluated WER of 29.0% on semantically-unpredictable sentences. This constitutes a significant improvement over our baseline voice trained on the same amount of randomly selected utterances, which performed at 42.4% WER. In addition to subjective voice evaluations with Amazon Mechanical Turk, we also explored objective voice evaluation using mel-cepstral distortion. We found that this measure correlates strongly with human evaluations of intelligibility, indicating that it may be a useful method to evaluate or pre-select voices in future work. 

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4. End-to-end Child-Adult Speech Diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model

   Kothalkar, P.V. ; Buzhardt, J. ; Irvin, D. ; Hansen, J.H.L. ( August 2022 , The journal of the Acoustical Society of America)

   Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs. controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates an alternate Deep Learning-based diarization solution for segmenting classroom interactions of 3-5 year old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our proposed ResNet model achieves a best F1-score of ∼71.0% on data from two classrooms, based on dev and test sets of each classroom. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs. child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations. 

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5. SpoTNet: A spoofing-aware Transformer Network for Effective Synthetic Speech Detection

   https://doi.org/10.1145/3592572.3592841  

   Khan, Awais ; Malik, Khalid Mahmood ( June 2023 , MAD '23: Proceedings of the 2nd ACM International Workshop on Multimedia AI against Disinformation)

   The prevalence of voice spoofing attacks in today’s digital world has become a critical security concern. Attackers employ various techniques, such as voice conversion (VC) and text-to-speech (TTS), to generate synthetic speech that imitates the victim’s voice and gain access to sensitive information. The recent advances in synthetic speech generation pose a significant threat to modern security systems, while traditional voice authentication methods are incapable of detecting them effectively. To address this issue, a novel solution for logical access (LA)-based synthetic speech detection is proposed in this paper. SpoTNet is an attention-based spoofing transformer network that includes crafted front-end spoofing features and deep attentive features retrieved using the developed logical spoofing transformer encoder (LSTE). The derived attentive features were then processed by the proposed multi-layer spoofing classifier to classify speech samples as bona fide or synthetic. In synthetic speeches produced by the TTS algorithm, the spectral characteristics of the synthetic speech are altered to match the target speaker’s formant frequencies, while in VC attacks, the temporal alignment of the speech segments is manipulated to preserve the target speaker’s prosodic features. By highlighting these observations, this paper targets the prosodic and phonetic-based crafted features, i.e., the Mel-spectrogram, spectral contrast, and spectral envelope, presenting an effective preprocessing pipeline proven to be effective in synthetic speech detection. The proposed solution achieved state-of-the-art performance against eight recent feature fusion methods with lower EER of 0.95% on the ASVspoof-LA dataset, demonstrating its potential to advance the field of speaker identification and improve speaker recognition systems. 

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