The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

How does the input segmentation of pretrained language models (PLMs) affect their
interpretations of complex words? We present the first study investigating this question …

Deep learning has emerged as a paradigm that revolutionizes numerous domains of
scientific research. Transformers have been utilized in language modeling outperforming …

An important research direction in automatic speech recognition (ASR) has centered around
the development of effective methods to rerank the output hypotheses of an ASR system with …

Transformers have evolved with great success in various artificial intelligence tasks. Thanks
to our recent prevalence of self-attention mechanisms, which capture long-term …

The current popular knowledge distillation (KD) methods effectively compress the
transformer-based end-to-end speech recognition model. However, existing methods fail to …

Conversational speech normally is embodied with loose syntactic structures at the utterance
level but simultaneously exhibits topical coherence relations across consecutive utterances …

The notion that it is difficult to make predictions about derivational morphology has been a
recurring theme in morphological research over the last decades. It can be unclear whether …

Spontaneous conversational Finnish is a challenging type of speech to recognise due to
frequent dysfluencies in sentence structure and the use of various informal wordforms. This …

Abstract Long Short-Term Memory (LSTM) cells, frequently used in state-of-the-art language
models, struggle with long sequences of inputs. One major problem in their design is that …