In this paper, we present CopyCat2 (CC2), a novel model capable of: a) synthesizing speech with different speaker identities, b) generating speech with expressive and contextually appropriate prosody, and c) transferring prosody at fine-grained level between any pair of seen speakers.We do this by activating distinct parts of the network for different tasks.We train our model using a novel approach to two-stage training.In Stage I, the model learns speaker-independent word-level prosody representations from speech which it uses for many-to-many finegrained prosody transfer.In Stage II, we learn to predict these prosody representations using the contextual information available in text, thereby, enabling multi-speaker TTS with contextually appropriate prosody.We compare CC2 to two strong baselines, one in TTS with contextually appropriate prosody, and one in fine-grained prosody transfer.CC2 reduces the gap in naturalness between our baseline and copy-synthesised speech by 22.79%.In fine-grained prosody transfer evaluations, it obtains a relative improvement of 33.15% in target speaker similarity.

Historical text normalization, the task of mapping historical word forms to their modern counterparts, has recently attracted a lot of interest (Bollmann, 2019;Tang et al., 2018;Lusetti et al., 2018;Bollmann et al., 2018;Robertson and Goldwater, 2018;Bollmann et al., 2017;Korchagina, 2017).Yet, virtually all approaches suffer from the two limitations: 1) They consider a fully supervised setup, often with impractically large manually normalized datasets; 2) Normalization happens on words in isolation.By utilizing a simple generative normalization model and obtaining powerful contextualization from the target-side language model, we train accurate models with unlabeled historical data.In realistic training scenarios, our approach often leads to reduction in manually normalized data at the same accuracy levels.

We present a neural transition-based model that uses a simple set of edit actions (copy, delete, insert) for morphological transduction tasks such as inflection generation, lemmatization, and reinflection. In a large-scale evaluation on four datasets and dozens of languages, our approach consistently outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. Learning to apply a generic copy action enables our approach to generalize quickly from a few data points. We successfully leverage minimum risk training to compensate for the weaknesses of MLE parameter learning and neutralize the negative effects of training a pipeline with a separate character aligner.

We employ imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization.Previous approaches to training this type of model either rely on an external character aligner for the production of gold action sequences, which results in a suboptimal model due to the unwarranted dependence on a single gold action sequence despite spurious ambiguity, or require warm starting with an MLE model.Our approach only requires a simple expert policy, eliminating the need for a character aligner or warm start.It also addresses familiar MLE training biases and leads to strong and state-of-the-art performance on several benchmarks.