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  1. Most current speech technology systems are designed to operate well even in the presence of multiple active speakers. However, most solutions assume that the number of co-current speakers is known. Unfortunately, this information might not always be available in real-world applications. In this study, we propose a real-time, single-channel attention-guided Convolutional Neural Network (CNN) to estimate the number of active speakers in overlapping speech. The proposed system extracts higher-level information from the speech spectral content using a CNN model. Next, the attention mechanism summarizes the extracted information into a compact feature vector without losing critical information. Finally, the active speakersmore »are classified using a fully connected network. Experiments on simulated overlapping speech using WSJ corpus show that the attention solution is shown to improve the performance by almost 3% absolute over conventional temporal average pooling. The proposed Attention-guided CNN achieves 76.15% for both Weighted Accuracy and average Recall, and 75.80% Precision on speech segments as short as 20 frames (i.e., 200 ms). All the classification metrics exceed 92% for the attention-guided model in offline scenarios where the input signal is more than 100 frames long (i.e., 1s).« less
    Free, publicly-accessible full text available September 1, 2022
  2. Speech and language development in children is crucial for ensuring optimal outcomes in their long term development and life-long educational journey. A child’s vocabulary size at the time of kindergarten entry is an early indicator of learning to read and potential long-term success in school. The preschool classroom is thus a promising venue for monitoring growth in young children by measuring their interactions with teachers and classmates. Automatic Speech Recognition (ASR) technologies provide the ability for ‘Early Childhood’ researchers for automatically analyzing naturalistic recordings in these settings. For this purpose, data are collected in a high-quality childcare center in themore »United States using Language Environment Analysis (LENA) devices worn by the preschool children. A preliminary task for ASR of daylong audio recordings would involve diarization, i.e., segmenting speech into smaller parts for identifying ‘who spoke when.’ This study investigates a Deep Learning-based diarization system for classroom interactions of 3-5-year-old children. However, the focus is on ’speaker group’ diarization, which includes classifying speech segments as being from adults or children from across multiple classrooms. SincNet based diarization systems achieve utterance level Diarization Error Rate of 19.1%. Utterance level speaker group confusion matrices also show promising, balanced results. These diarization systems have potential applications in developing metrics for adult-to-child or child-to-child rapid conversational turns in a naturalistic noisy early childhood setting. Such technical advancements will also help teachers better and more efficiently quantify and understand their interactions with children, make changes as needed, and monitor the impact of those changes.« less
    Free, publicly-accessible full text available March 1, 2022
  3. A key goal of Next Generation Science Standards is to promote interest and exploration of natural phenomena. In preschool settings, teachers prompt exploration by asking questions, encouraging informal exploration and experimentation. To date, live or offline video observation has been the sole way to capture the quality of teacher question asking in the pre-k classroom (e.g., Sanders et al., 2016). To date, Automatic Speech Recognition (ASR) has not been used to measure the content/quality of teacher talk. Here, we used ASR to quantify preschool teachers’ use of keywords that promote student exploration and inquiry.