The field of speech processing is witnessing significant advancements, with a focus on improving speech translation, audio coding, and speech recognition. Researchers are exploring innovative architectures and techniques to enhance the efficiency and accuracy of these systems. Notably, there is a growing interest in developing speech translation models that can handle complex linguistic structures and nuances, such as idiom translation and speaker gender identification. Additionally, advancements in audio coding are enabling the development of high-fidelity neural audio codecs that can compress speech and music effectively.
Some noteworthy papers in this area include: HENT-SRT, which proposes a novel framework for joint speech recognition and translation using a hierarchical efficient neural transducer with self-distillation. SwitchCodec, which introduces a high-fidelity neural audio codec with sparse quantization, achieving improved performance while maintaining low latency. MFLA, which presents a novel prefix-to-prefix training framework for streaming speech recognition using monotonic finite look-ahead attention.
