Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies. The proposed neural network architecture is capable of modeling multiple relation instances without knowing the corresponding relation arguments in a sentence. The experimental results show that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance. We present state-of-the-art results with improvements of 2.0% and 2.7% for entity recognition and relation classification, respectively on CoNLL04 dataset.

This paper presents a comparative study of four different approaches to automatic age and gender classification using seven classes on a telephony speech task and also compares the results with human performance on the same data. The automatic approaches compared are based on (1) a parallel phone recognizer, derived from an automatic language identification system; (2) a system using dynamic Bayesian networks to combine several prosodic features; (3) a system based solely on linear prediction analysis; and (4) Gaussian mixture models based on MFCCs for separate recognition of age and gender. On average, the parallel phone recognizer performs as well as Human listeners do, while loosing performance on short utterances. The system based on prosodic features however shows very little dependence on the length of the utterance.

In order to make hidden Markov model (HMM) speech recognition suitable for mobile phone applications, Siemens developed a recognizer, Very Smart Recognizer (VSR), for deployment in future mobile phone generations. Typical applications will be name dialling, command and control operations suited for different environments, for example in cars. The paper describes research and development issues of a speech recognizer in mobile devices focusing on noise robustness, memory efficiency and integer implementation. The VSR is shown to reach a word error rate as low as 4.1% on continuous digits recorded in a car environment. Furthermore by means of discriminative training and HMM-parameter coding, the memory requirements of the VSR HMMs are smaller than 64 kBytes.

Abstract Following the objective of the Eurospeech special event,'Noise Robust Recognition', the recognition results of a noiserobust front-end, developed by Siemens, on the Aurora 2 da-tabase [1] are presented in this paper. The front-end wastested with and without a frame dropping algorithm. It isshown that the front-end improves the recognition results inhigh mismatch between training and testing by 43.90% overthe reference front-end and works particularily well in con-ditions with high noise. Furthermore it is shown that theframe dropping mainly increases the performance of the front-end. 1. Introduction The front-end employed is a cepstral analysis scheme withspectral attenuation and spectral subtraction, a channel com-pensation, calculation of the time derivatives, an LDA and aframe dropping algorithm (Figure 1).Details of the front-end can be seen from Table 1. The spe-cific algorithms are shortly described in the following.Parameter settings of the front-endFrame length: 32 msFrame shift: 15 msNumber of samples per frame, NF: 256Preemphasis factor: 0.95Lenght of FFT, FFTL: 256Number of Mel filter banks: 15Number of cepstral coefficients: 12Number of first derivatives: 13Number of second derivatives: 13Size of the feature vector: 24Table 1: Parameter settings of the front-end