2,879 research outputs found
A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)
The alignment of heterogeneous sequential data (video to text) is an
important and challenging problem. Standard techniques for this task, including
Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs), suffer from
inherent drawbacks. Mainly, the Markov assumption implies that, given the
immediate past, future alignment decisions are independent of further history.
The separation between similarity computation and alignment decision also
prevents end-to-end training. In this paper, we propose an end-to-end neural
architecture where alignment actions are implemented as moving data between
stacks of Long Short-term Memory (LSTM) blocks. This flexible architecture
supports a large variety of alignment tasks, including one-to-one, one-to-many,
skipping unmatched elements, and (with extensions) non-monotonic alignment.
Extensive experiments on semi-synthetic and real datasets show that our
algorithm outperforms state-of-the-art baselines.Comment: Accepted at CVPR 2018 (Spotlight). arXiv file includes the paper and
the supplemental materia
Combining vocal tract length normalization with hierarchial linear transformations
Recent research has demonstrated the effectiveness of vocal tract length normalization (VTLN) as a rapid adaptation technique for statistical parametric speech synthesis. VTLN produces speech with naturalness preferable to that of MLLR-based adaptation techniques, being much closer in quality to that generated by the original av-erage voice model. However with only a single parameter, VTLN captures very few speaker specific characteristics when compared to linear transform based adaptation techniques. This paper pro-poses that the merits of VTLN can be combined with those of linear transform based adaptation in a hierarchial Bayesian frame-work, where VTLN is used as the prior information. A novel tech-nique for propagating the gender information from the VTLN prior through constrained structural maximum a posteriori linear regres-sion (CSMAPLR) adaptation is presented. Experiments show that the resulting transformation has improved speech quality with better naturalness, intelligibility and improved speaker similarity. Index Terms — Statistical parametric speech synthesis, hidden Markov models, speaker adaptation, vocal tract length normaliza-tion, constrained structural maximum a posteriori linear regression 1
A Pattern Matching method for finding Noun and Proper Noun Translations from Noisy Parallel Corpora
We present a pattern matching method for compiling a bilingual lexicon of
nouns and proper nouns from unaligned, noisy parallel texts of
Asian/Indo-European language pairs. Tagging information of one language is
used. Word frequency and position information for high and low frequency words
are represented in two different vector forms for pattern matching. New anchor
point finding and noise elimination techniques are introduced. We obtained a
73.1\% precision. We also show how the results can be used in the compilation
of domain-specific noun phrases.Comment: 8 pages, uuencoded compressed postscript file. To appear in the
Proceedings of the 33rd AC
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