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Recently End-to-end (E2E) Automatic Speech Recognition (ASR) has been widely used due to its advantages over the hybrid method. Even though existing E2E ASR models have achieved impressive performance, they usually take a large model size and suffer from a slow inference speed in real-world applications. To obtain faster models for E2E ASR, we propose searching faster architectures with the help of neural architecture search (NAS) in this paper, named SFA. SFA consists of one search space that contains a set of candidate architectures and one search algorithm responsible for searching the optimal architecture from the search space. On one hand, SFA designs a topology-fused search space to integrate different topologies of existing architectures (e.g. Transformer, Conformer) and explore more brand-new ones. On the other hand, combined with the training criterion of E2E ASR, SFA develops a speed-aware differentiable search algorithm to search faster architectures according to target hardware devices. Additionally, a connectionist temporal classification based progressive search algorithm is proposed to reduce the difficulty of the architecture search and obtain better performance. On two commonly-used Mandarin datasets, SFA can effectively improve the inference speed of existing E2E ASR models with comparable performance and achieve at most 2. 46 × / 1. 98 × CPU/GPU speedup than the best human-designed baselines. • E2E ASR suffers from a slow inference speed. • The proposed SFA platform designs fasterarchitectures with the for E2E ASR. • A topology-fused search space provides adequate candidate architectures. • A speed-aware search algorithm selects promising architectures. • An obvious speedup can be achieved with SFA. [ABSTRACT FROM AUTHOR]

Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify "who spoke when". In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing. These algorithms also gained their own value as a standalone application over time to provide speaker-specific metainformation for downstream tasks such as audio retrieval. More recently, with the emergence of deep learning technology, which has driven revolutionary changes in research and practices across speech application domains, rapid advancements have been made for speaker diarization. In this paper, we review not only the historical development of speaker diarization technology but also the recent advancements in neural speaker diarization approaches. Furthermore, we discuss how speaker diarization systems have been integrated with speech recognition applications and how the recent surge of deep learning is leading the way of jointly modeling these two components to be complementary to each other. By considering such exciting technical trends, we believe that this paper is a valuable contribution to the community to provide a survey work by consolidating the recent developments with neural methods and thus facilitating further progress toward a more efficient speaker diarization. • The latest trends and approaches to speaker diarization as part of speech interaction applications. • Overview of the development of speaker diarization in the era of deep learning. • Review of diarization techniques belonging to the proposed taxonomy. • Introduction of techniques used in the traditional, modular speaker diarization systems. • Recent advancements in joint training approaches and fully end-to-end models. • A perspective of how speaker diarization has been investigated in the context of ASR. • Review of the challenges, the future of speaker diarization and the applications of speaker diarization. [ABSTRACT FROM AUTHOR]