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1. Sequence effects and speech processing: cognitive load for speaker-switching within and across accents

   https://doi.org/10.3758/s13423-023-02322-1  

   McLaughlin, Drew J. ; Colvett, Jackson S. ; Bugg, Julie M. ; Van Engen, Kristin J. ( July 2023 , Psychonomic Bulletin & Review)

   Prior work in speech processing indicates that listening tasks with multiple speakers (as opposed to a single speaker) result in slower and less accurate processing. Notably, the trial-to-trial cognitive demands of switching between speakers or switching between accents have yet to be examined. We used pupillometry, a physiological index of cognitive load, to examine the demands of processing first (L1) and second (L2) language-accented speech when listening to sentences produced by the same speaker consecutively (no switch), a novel speaker of the same accent (within-accent switch), and a novel speaker with a different accent (across-accent switch). Inspired by research on sequential adjustments in cognitive control, we aimed to identify the cognitive demands of accommodating a novel speaker and accent by examining the trial-to-trial changes in pupil dilation during speech processing. Our results indicate that switching between speakers was more cognitively demanding than listening to the same speaker consecutively. Additionally, switching to a novel speaker with a different accent was more cognitively demanding than switching between speakers of the same accent. However, there was an asymmetry for across-accent switches, such that switching from an L1 to an L2 accent was more demanding than vice versa. Findings from the present study align with work examining multi-talker processing costs, and provide novel evidence that listeners dynamically adjust cognitive processing to accommodate speaker and accent variability. We discuss these novel findings in the context of an active control model and auditory streaming framework of speech processing.

    

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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. Speaker discrimination performance for “easy” versus “hard” voices in style-matched and -mismatched speech

   https://doi.org/10.1121/10.0009585  

   Afshan, Amber ; Kreiman, Jody ; Alwan, Abeer ( February 2022 , The Journal of the Acoustical Society of America)

   This study compares human speaker discrimination performance for read speech versus casual conversations and explores differences between unfamiliar voices that are “easy” versus “hard” to “tell together” versus “tell apart.” Thirty listeners were asked whether pairs of short style-matched or -mismatched, text-independent utterances represented the same or different speakers. Listeners performed better when stimuli were style-matched, particularly in read speech−read speech trials (equal error rate, EER, of 6.96% versus 15.12% in conversation–conversation trials). In contrast, the EER was 20.68% for the style-mismatched condition. When styles were matched, listeners' confidence was higher when speakers were the same versus different; however, style variation caused decreases in listeners' confidence for the “same speaker” trials, suggesting a higher dependency of this task on within-speaker variability. The speakers who were “easy” or “hard” to “tell together” were not the same as those who were “easy” or “hard” to “tell apart.” Analysis of speaker acoustic spaces suggested that the difference observed in human approaches to “same speaker” and “different speaker” tasks depends primarily on listeners' different perceptual strategies when dealing with within- versus between-speaker acoustic variability.

    

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4. Performance on stochastic figure-ground perception varies with individual differences in speech-in-noise recognition and working memory capacity

   https://doi.org/10.1121/10.0016756  

   Johns, Michael A. ; Calloway, Regina C. ; Phillips, Ian ; Karuzis, Valerie P. ; Dutta, Kelsey ; Smith, Ed ; Shamma, Shihab A. ; Goupell, Matthew J. ; Kuchinsky, Stefanie E. ( January 2023 , The Journal of the Acoustical Society of America)

   Speech recognition in noisy environments can be challenging and requires listeners to accurately segregate a target speaker from irrelevant background noise. Stochastic figure-ground (SFG) tasks in which temporally coherent inharmonic pure-tones must be identified from a background have been used to probe the non-linguistic auditory stream segregation processes important for speech-in-noise processing. However, little is known about the relationship between performance on SFG tasks and speech-in-noise tasks nor the individual differences that may modulate such relationships. In this study, 37 younger normal-hearing adults performed an SFG task with target figure chords consisting of four, six, eight, or ten temporally coherent tones amongst a background of randomly varying tones. Stimuli were designed to be spectrally and temporally flat. An increased number of temporally coherent tones resulted in higher accuracy and faster reaction times (RTs). For ten target tones, faster RTs were associated with better scores on the Quick Speech-in-Noise task. Individual differences in working memory capacity and self-reported musicianship further modulated these relationships. Overall, results demonstrate that the SFG task could serve as an assessment of auditory stream segregation accuracy and RT that is sensitive to individual differences in cognitive and auditory abilities, even among younger normal-hearing adults.

    

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5. Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task

   https://doi.org/10.1109/EMBC.2019.8857953  

   Presacco, Alessandro ; Miran, Sina ; Babadi, Behtash ; Simon, Jonathan Z ( July 2019 , 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC))

   In the last few years, a large number of experiments have been focused on exploring the possibility of using non-invasive techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), to identify auditory-related neuromarkers which are modulated by attention. Results from several studies where participants listen to a story narrated by one speaker, while trying to ignore a different story narrated by a competing speaker, suggest the feasibility of extracting neuromarkers that demonstrate enhanced phase locking to the attended speech stream. These promising findings have the potential to be used in clinical applications, such as EEG-driven hearing aids. One major challenge in achieving this goal is the need to devise an algorithm capable of tracking these neuromarkers in real-time when individuals are given the freedom to repeatedly switch attention among speakers at will. Here we present an algorithm pipeline that is designed to efficiently recognize changes of neural speech tracking during a dynamic-attention switching task and to use them as an input for a near real-time state-space model that translates these neuromarkers into attentional state estimates with a minimal delay. This algorithm pipeline was tested with MEG data collected from participants who had the freedom to change the focus of their attention between two speakers at will. Results suggest the feasibility of using our algorithm pipeline to track changes of attention in near-real time in a dynamic auditory scene. 

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