Responses to seasonal changes in nutrient quality and patchiness of food in a multigroup community of Tibetan macaques at Mt. Emei

Provisioning along pedestrian trails by tourists much increased the nutrient quality and patchiness of food (NqPF)for Tibetan macaques (Macaca thibetana) at Mt Emei in spring and summer. In the habitat at a temperate-subtropical transition zone, the mncaque's NqPF could be ordered in a decreasing rank from spring summer to autumn to winter With the aid of a radio-tracking system, I collected ranging data on a multigroup community in three 70-day periods representing the different seasons in 1991-92, Rank-order correlation on the data show that with the decline of NqPF; the groups tended to increase days away from the trail, their effective range size (ERS) their exclusive area (EA) and the number of days spent in the EA, and reduced their group/community density and the ratio of the overlapped range to the seasonal range (ROR). In icy/snowy winter; the macaques searched for mature leaves slowly and carefully in the largest seasonal range with a considerable portion that was nor used in other seasons. Of the responses, the ROR decreased with the reduction in group/community density; and the ERS was the function of both group size (+) and intergroup rank (-) when favorite food was highly clumped. All above responses were clearly bound to maximize foraging effectiveness and minimize energy expenditure, and their integration in term of changes in time and space leads to better understanding macaque ecological adaptability. Based on this study and previous work on behavioral and physiological factors, I suggest a unifying theory of intergroup interactions. Ir! addition, as the rate of behavioral interactions,was also related to the group density, I Waser's (1976) gas model probably applies to behavioral, as well as spatial, data on intergroup interactions.

The reproductive strategies of an endemic Tibetan fish, Gymnocypris selincuoensis

The reproductive traits of Gymnocypris selincuoensis from Selincuo Lake and its tributaries were investigated in 1997 and 1998. The youngest mature male was age 7 with a standard length (SL) of 172.0 mm, and the youngest mature female was age 8 with a SL of 194.0 mm. The L(50)s Of SL and age at first maturity were respectively 250.32 mm and age 9 for males and 224.71 mm and age 8 for females. The gonadosomatic index (GSI) significantly changed with seasons for mature individuals but not for immature individuals. GSIs of mature females at stages IV and V of ovary development increased with SL and reached a maximum value at the SL range from 370 mm to 390 mm; the GSIs of mature males were negatively correlated with SL. The breeding season lasted from early April to early August. Egg size did not significantly change with SL but increased with the delay of spawning. The individual absolute fecundity varied from 1,341 to 28,002 eggs (mean 12,607+/-7,349), and the individual relative fecundity varied from 6.4 to 42.0 eggs.g(-1) (mean 25.5+/-9.7). The individual fecundity increased with total body weight; it also increased with SL for those of SL less than 370 mm. There was a rest of spawning for mature individuals.

Molecular phylogeny of the specialized schizothoracine fishes (Teleostei : Cyprinidae), with their implications for the uplift of the Qinghai-Tibetan Plateau

Molecular phylogeny of three genera containing nine species and subspecies of the specialized schizothoracine fishes are investigated based on the complete nucleotide sequence of mitochondrial cytochrome b gene. Meantime relationships between the main cladogenetic events of the specialized schizothoracine fishes and the stepwise uplift of the Qinghai-Tibetan Plateau are also conducted using the molecular clock, which is calibrated by geological isolated events between the upper reaches of the Yellow River and the Qinghai Lake. Results indicated that the specialized schizothoracine fishes are not a monophyly. Five species and subspecies of Ptychobarbus form a monophyly. But three species of Gymnodiptychus do not form a monophyly. Gd. integrigymnatus is a sister taxon of the highly specialized schizothoracine fishes while Gd. pachycheilus has a close relation with Gd. dybowskii, and both of them are as a sister group of Diptychus maculatus. The specialized schizothoracines fishes might have originated during the Miocene (about 10 MaBP), and then the divergence of three genera happened during late Miocene (about 8 MaBP). Their main specialization occurred during the late Pliocene and Pleistocene (3.54-0.42 MaBP). The main cladogenetic events of the specialized schizothoracine fishes are mostly correlated with the geological tectonic events and intensive climate shift happened at 8, 3.6, 2.5 and 1.7 MaBP of the late Cenozoic. Molecular clock data do not support the hypothesis that the Qinghai-Tibetan Plateau uplifted to near present or even higher elevations during the Oligocene or Miocene, and neither in agreement with the view that the plateau uplifting reached only to an altitude of 2000 in during the late Pliocene (about 2.6 MaBP).

Statistical parametric speech synthesis based on speaker and language factorization

An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize speaker-/language-specific characteristics in the data and then model them using separate transforms. Language-specific factors in the data are represented by transforms based on cluster mean interpolation with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by transforms based on constrained maximum-likelihood linear regression. Experimental results on statistical parametric speech synthesis show that the proposed framework enables data from multiple speakers in different languages to be used to: train a synthesis system; synthesize speech in a language using speaker characteristics estimated in a different language; and adapt to a new language. © 2012 IEEE.

Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.

Discriminative spoken language understanding using word confusion networks

Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output. © 2012 IEEE.

Language model cross adaptation for LVCSR system combination

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple sub-systems that may even be developed at different sites. Cross system adaptation, in which model adaptation is performed using the outputs from another sub-system, can be used as an alternative to hypothesis level combination schemes such as ROVER. Normally cross adaptation is only performed on the acoustic models. However, there are many other levels in LVCSR systems' modelling hierarchy where complimentary features may be exploited, for example, the sub-word and the word level, to further improve cross adaptation based system combination. It is thus interesting to also cross adapt language models (LMs) to capture these additional useful features. In this paper cross adaptation is applied to three forms of language models, a multi-level LM that models both syllable and word sequences, a word level neural network LM, and the linear combination of the two. Significant error rate reductions of 4.0-7.1% relative were obtained over ROVER and acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. © 2012 Elsevier Ltd. All rights reserved.

Paraphrastic language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, this paper presents a novel form of language model, the paraphrastic LM. A phrase level transduction model that is statistically learned from standard text data is used to generate paraphrase variants. LM probabilities are then estimated by maximizing their marginal probability. Significant error rate reductions of 0.5%-0.6% absolute were obtained on a state-ofthe-art conversational telephone speech recognition task using a paraphrastic multi-level LM modelling both word and phrase sequences.

Paraphrastic language models and combination with neural network language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, paraphrastic LMs were proposed in previous research and successfully applied to a US English conversational telephone speech transcription task. In order to exploit the complementary characteristics of paraphrastic LMs and neural network LMs (NNLM), the combination between the two is investigated in this paper. To investigate paraphrastic LMs' generalization ability to other languages, experiments are conducted on a Mandarin Chinese broadcast speech transcription task. Using a paraphrastic multi-level LM modelling both word and phrase sequences, significant error rate reductions of 0.9% absolute (9% relative) and 0.5% absolute (5% relative) were obtained over the baseline n-gram and NNLM systems respectively, after a combination with word and phrase level NNLMs. © 2013 IEEE.

Comparative phylogeography of the Yellow River schizothoracine fishes (Cyprinidae): Vicariance, expansion, and recent coalescence in response to the Quaternary environmental upheaval in the Tibetan Plateau

National Natural Science Foundation of China (NSFC) [30225008, 30300036, 30530120]; Key Innovation Plan [KSCX2-SW-106]; National Basic Research Project in China [2005cb422005]; National Natural Science Foundation of China [30600062]