Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction

This paper demonstrates how unsupervised cross-lingual adaptation of HMM-based speech synthesis models may be performed without explicit knowledge of the adaptation data language. A two-pass decision tree construction technique is deployed for this purpose. Using parallel translated datasets, cross-lingual and intralingual adaptation are compared in a controlled manner. Listener evaluations reveal that the proposed method delivers performance approaching that of unsupervised intralingual adaptation

Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation

SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models. In this paper, we describe three use cases in which SGNMT is currently playing an active role: (1) teaching as SGNMT is being used for course work and student theses in the MPhil in Machine Learning, Speech and Language Technology at the University of Cambridge, (2) research as most of the research work of the Cambridge MT group is based on SGNMT, and (3) technology transfer as we show how SGNMT is helping to transfer research findings from the laboratory to the industry, eg. into a product of SDL plc

Neural Machine Translation Decoding with Terminology Constraints

Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology con- straints remains an open problem. We describe our approach to constrained neural decod- ing based on finite-state machines and multi- stack decoding which supports target-side con- straints as well as constraints with correspond- ing aligned input text spans. We demonstrate the performance of our framework on multiple translation tasks and motivate the need for constrained decoding with attentions as a means of reducing misplacement and duplication when translating user constraints

Multi-representation Ensembles and Delayed SGD Updates Improve Syntax-based NMT

We explore strategies for incorporating target syntax into Neural Machine Translation. We specifically focus on syntax in ensembles containing multiple sentence representations. We formulate beam search over such ensembles using WFSTs, and describe a delayed SGD update training procedure that is especially effective for long representations like linearized syntax. Our approach gives state-of-the-art performance on a difficult Japanese-English task.This work was supported by EPSRC grant EP/L027623/1

Autoregressive clustering for HMM speech synthesis

The autoregressive HMM has been shown to provide efficient parameter estimation and high-quality synthesis, but in previous experiments decision trees derived from a non-autoregressive system were used. In this paper we investigate the use of autoregressive clustering for autoregressive HMM-based speech synthesis. We describe decision tree clustering for the autoregressive HMM and highlight differences to the standard clustering procedure. Subjective listening evaluation results suggest that autoregressive clustering improves the naturalness of the resulting speech. We find that the standard minimum description length (MDL) criterion for selecting model complexity is inappropriate for the autoregressive HMM. Investigating the effect of model complexity on naturalness, we find that a large degree of overfitting is tolerated without a substantial decrease in naturalness.This research was funded by the European Community's Seventh Framework Programme (FP7/2007-2013), grant agreement 213845 (EMIME)

Autoregressive HMMs for speech synthesis

We propose the autoregressive HMM for speech synthesis. We show that the autoregressive HMM supports efficient EM parameter estimation and that we can use established effective synthesis techniques such as synthesis considering global variance with minimal modification. The autoregressive HMM uses the same model for parameter estimation and synthesis in a consistent way, in contrast to the standard HMM synthesis framework, and supports easy and efficient parameter estimation, in contrast to the trajectory HMM. We find that the autoregressive HMM gives performance comparable to the standard HMM synthesis framework on a Blizzard Challenge-style naturalness evaluation.This research was funded by the European Community's Seventh Framework Programme (FP7/2007-2013), grant agreement 213845 (EMIME)

Source sentence simplification for statistical machine translation

Long sentences with complex syntax and long-distance dependencies pose difficulties for machine translation systems. Short sentences, on the other hand, are usually easier to translate. We study the potential of addressing this mismatch using text simplifi- cation: given a simplified version of the full input sentence, can we use it in addition to the full input to improve translation? We show that the spaces of original and simplified translations can be effectively combined using translation lattices and compare two decoding approaches to process both inputs at different levels of integration. We demonstrate on source-annotated portions of WMT test sets and on top of strong baseline systems combining hierarchical and neural translation for two language pairs that source simplification can help to improve translation quality.This work was supported by the EPSRC grant Improving Target Language Fluency in Statistical Machine Translation, grant number EP/L027623/1

Controlling the Optical Properties of a Conjugated Co-polymer through Variation of Backbone Isomerism and the Introduction of Carbon Nanotubes

The need to control the formation of weakly emitting species in polymers such as aggregates and excimers, which are normally detrimental to device performance, is illustrated for the example of the polymer poly(m-phenylenevinylene-co-2,5-dioctyloxy-p-phenylenevinylene), using the model compound, 2,5-dioctyloxy-p-distyrylbenzene as a comparison. Two different methods, namely a Homer-Emmons polycondensation in dimethylformamide (DMF) and a Wittig polycondensation in dry toluene, have been used during synthesis resulting in a polymer with a predominantly trans-vinylene backbone and a polymer with a predominantly cis-vinylene backbone, respectively. Photoluminescence and absorption spectroscopy indicate that the polymer forms aggregate species in solution with spectra that are distinctly red-shifted from those associated with the intra-chain exciton. Concentration dependent optical studies were used to probe the evolution of aggregation in solution for both polymers. The results indicate that inter-chain coupling in the predominantly cis-polymer is prominent at lower concentrations than in the case of the trans-counterpart. These results are supported by pico-second pump and probe transient absorption measurements where, in dilute solutions, the polymer in a cis-configuration exhibits highly complex excited state dynamics, whereas the polymer in a trans-configuration behaves similarly to the model compound. It is proposed therefore that the degree of backbone isomerism has a profound impact on the morphology of the polymeric solid and control over it is a route towards optimising the performance of the material in thin film form. Another method to inhibit inter-chain effects using multi walled carbon nanotubes (MWNT) as nano-spacers in the polymer solutions is proposed. By comparison to spectroscopic analysis, aggregation effects are shown to be reduced by the introduction of nanotubes. Electron microscopy and computer simulation suggest a well-defined interaction between the polymer backbone and the lattice of the nanotube

Break it Down for Me: A Study in Automated Lyric Annotation

Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can under- stand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understand- able manner. However, in ALA the system must often include additional infor- mation to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.Research Foundation - Flanders (FWO) and U.K. Engineering and Physical Sciences Research Council (EPSRC grant EP/L027623/1

Able-Bodied Wild Chimpanzees Imitate a Motor Procedure Used by a Disabled Individual to Overcome Handicap

Chimpanzee culture has generated intense recent interest, fueled by the technical complexity of chimpanzee tool-using traditions; yet it is seriously doubted whether chimpanzees are able to learn motor procedures by imitation under natural conditions. Here we take advantage of an unusual chimpanzee population as a ‘natural experiment’ to identify evidence for imitative learning of this kind in wild chimpanzees. The Sonso chimpanzee community has suffered from high levels of snare injury and now has several manually disabled members. Adult male Tinka, with near-total paralysis of both hands, compensates inability to scratch his back manually by employing a distinctive technique of holding a growing liana taut while making side-to-side body movements against it. We found that seven able-bodied young chimpanzees also used this ‘liana-scratch’ technique, although they had no need to. The distribution of the liana-scratch technique was statistically associated with individuals' range overlap with Tinka and the extent of time they spent in parties with him, confirming that the technique is acquired by social learning. The motivation for able-bodied chimpanzees copying his variant is unknown, but the fact that they do is evidence that the imitative learning of motor procedures from others is a natural trait of wild chimpanzees