Conversational AI

Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands. The endpoint detector has to trade-off between accuracy and latency, since waiting longer reduces the cases of users being cut-off early. We propose a novel two-pass solution for endpointing, where the utterance endpoint detected from a first pass endpointer is verified by a 2nd-pass

In recent years, the field of natural language processing (NLP) has witnessed remarkable advancements driven by the development of large language models (LLMs). Various techniques, such as instruction tuning, have emerged as crucial approaches, enhancing LLMs’ adaptability to new tasks guided by instructional prompts. Meanwhile, the phenomenon of memorization within LLMs has garnered considerable attention

Statistical significance testing is used in natural language processing (NLP) to determine whether the results of a study or experiment are likely to be due to chance or if they reflect a genuine relationship. A key step in significance testing is the estimation of confidence interval which is a function of sample variance. Sample variance calculation is straightforward when evaluating against ground truth

To boost training and adaptation of end to end (E2E) automatic speech recognition (ASR) models, several approaches to use paired speech-text input together with unpaired text input have emerged. They aim at improving the model performance on rare words, personalisation, and long tail. In this work, we present a systematic study of the impact of such training/adaptation and compare it to training with synthetic

While recent studies have looked into the abilities of large language models in various benchmark tasks, few studies have looked into the controllability of large language models on generation tasks. We present a systematic and extensive analysis of the controllability of large language models on ten benchmarks, including a new simple yet challenging numerical planning benchmark with different granularities