No abstract

Purpose The purpose of this paper is to identify the nature and factors that influence student evaluation of the teaching performance of university teachers by integrating two areas of research: services marketing and higher education. Design/methodology/approach A set of hypotheses were developed taking into consideration customer (student), employee (teacher) and service (course) characteristics. They were then tested using data from 952 courses for a three‐year period and employing different multivariate techniques. Findings Students basically evaluate the expertise, attitude and behavior of teachers. The results also indicate that this evaluation is a complex phenomenon that depends on factors related to teacher, student and course profiles. Research limitations/implications Given the nature of the data used here, future studies should extend the scope of research to other institutions, examine quality from an objective standpoint and include teachers’ perceptions and the outcomes of their research activity. Practical implications Based on the results of this paper, the authors recommend the following: to permit teachers to teach the same courses repeatedly, allowing them to consolidate their practice; to provide training in teaching techniques and ethics; to pay particular attention to those students who move to another degree program; and to maintain an appropriate class size. Originality/value This study integrates two areas of research and proposes a wide range of service quality determinants in the context of higher education, including several factors that had not been previously considered.

In this paper, we describe the implementation and evaluation of Text to Speech synthesizers based on neural networks for Spanish and Basque. Several voices were built, all of them using a limited number of data. The system applies Tacotron 2 to compute mel-spectrograms from the input sequence, followed by WaveGlow as neural vocoder to obtain the audio signals from the spectrograms. The limited number of data used for training the models leads to synthesis errors in some sentences. To automatically detect those errors, we developed a new method that is able to find the sentences that have lost the alignment during the inference process. To mitigate the problem, we implemented a guided attention providing the system with the explicit duration of the phonemes. The resulting system was evaluated to assess its robustness, quality and naturalness both with objective and subjective measures. The results reveal the capacity of the system to produce good quality and natural audios.

No abstract