Characterisation of noisy speech channels in 2G and 3G mobile networks

As the wireless cellular market reaches competitive levels never seen before, network operators need to focus on maintaining Quality of Service (QoS) a main priority if they wish to attract new subscribers while keeping existing customers satisfied. Speech Quality as perceived by the end user is one major example of a characteristic in constant need of maintenance and improvement. It is in this topic that this Master Thesis project fits in. Making use of an intrusive method of speech quality evaluation, as a means to further study and characterize the performance of speech codecs in second-generation (2G) and third-generation (3G) technologies. Trying to find further correlation between codecs with similar bit rates, along with the exploration of certain transmission parameters which may aid in the assessment of speech quality. Due to some limitations concerning the audio analyzer equipment that was to be employed, a different system for recording the test samples was sought out. Although the new designed system is not standard, after extensive testing and optimization of the system's parameters, final results were found reliable and satisfactory. Tests include a set of high and low bit rate codecs for both 2G and 3G, where values were compared and analysed, leading to the outcome that 3G speech codecs perform better, under the approximately same conditions, when compared with 2G. Reinforcing the idea that 3G is, with no doubt, the best choice if the costumer looks for the best possible listening speech quality. Regarding the transmission parameters chosen for the experiment, the Receiver Quality (RxQual) and Received Energy per Chip to the Power Density Ratio (Ec/N0), these were subject to speech quality correlation tests. Final results of RxQual were compared to those of prior studies from different researchers and, are considered to be of important relevance. Leading to the confirmation of RxQual as a reliable indicator of speech quality. As for Ec/N0, it is not possible to state it as a speech quality indicator however, it shows clear thresholds for which the MOS values decrease significantly. The studied transmission parameters show that they can be used not only for network management purposes but, at the same time, give an expected idea to the communications engineer (or technician) of the end-to-end speech quality consequences. With the conclusion of the work new ideas for future studies come to mind. Considering that the fourth-generation (4G) cellular technologies are now beginning to take an important place in the global market, as the first all-IP network structure, it seems of great relevance that 4G speech quality should be subject of evaluation. Comparing it to 3G, not only in narrowband but also adding wideband scenarios with the most recent standard objective method of speech quality assessment, POLQA. Also, new data found on Ec/N0 tests, justifies further research studies with the intention of validating the assumptions made in this work.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Speech as the basic interface for assistive technology

Speech interfaces for Assistive Technologies are not common and are usually replaced by others. The market they are targeting is not considered attractive and speech technologies are still not well spread. Industry still thinks they present some performance risks, especially Speech Recognition systems. As speech is the most elemental and natural way for communication, it has strong potential for enhancing inclusion and quality of life for broader groups of users with special needs, such as people with cerebral palsy and elderly staying at their homes. This work is a position paper in which the authors argue for the need to make speech become the basic interface in assistive technologies. Among the main arguments, we can state: speech is the easiest way to interact with machines; there is a growing market for embedded speech in assistive technologies, since the number of disabled and elderly people is expanding; speech technology is already mature to be used but needs adaptation to people with special needs; there is still a lot of R&D to be done in this area, especially when thinking about the Portuguese market. The main challenges are presented and future directions are proposed.

Automatic syllabification for danish text-to-speech systems

In this paper, a rule-based automatic syllabifier for Danish is described using the Maximal Onset Principle. Prior success rates of rule-based methods applied to Portuguese and Catalan syllabification modules were on the basis of this work. The system was implemented and tested using a very small set of rules. The results gave rise to 96.9% and 98.7% of word accuracy rate, contrary to our initial expectations, being Danish a language with a complex syllabic structure and thus difficult to be rule-driven. Comparison with data-driven syllabification system using artificial neural networks showed a higher accuracy rate of the former system.

Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system’s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

Development of The Viking Speech Scale to Classify the Speech of Children with Cerebral Palsy

Surveillance registers monitor the prevalence of cerebral palsy and the severity of resulting impairments across time and place. The motor disorders of cerebral palsy can affect children’s speech production and limit their intelligibility. We describe the development of a scale to classify children’s speech performance for use in cerebral palsy surveillance registers, and its reliability across raters and across time. Speech and language therapists, other healthcare professionals and parents classified the speech of 139 children with cerebral palsy (85 boys, 54 girls; mean age 6.03 years, SD 1.09) from observation and previous knowledge of the children. Another group of health professionals rated children’s speech from information in their medical notes. With the exception of parents, raters reclassified children’s speech at least four weeks after their initial classification. Raters were asked to rate how easy the scale was to use and how well the scale described the child’s speech production using Likert scales. Inter-rater reliability was moderate to substantial (k > .58 for all comparisons). Test–retest reliability was substantial to almost perfect for all groups (k > .68). Over 74% of raters found the scale easy or very easy to use; 66% of parents and over 70% of health care professionals judged the scale to describe children’s speech well or very well. We conclude that the Viking Speech Scale is a reliable tool to describe the speech performance of children with cerebral palsy, which can be applied through direct observation of children or through case note review.

Repairs of databases with null values

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Integration of speech-processing technologies into Activobank's client interaction process

This dissertation analyzes the possibilities of utilizing speech-processing technologies to transform the user experience of ActivoBank’s customers while using remote banking solutions. The technologies are examined through different criteria to determine if they support the bank’s goals and strategy and whether they should be incorporated in the bank’s offering. These criteria include the alignment with ActivoBank’s values, the suitability of the technology providers, the benefits these technologies entail, potential risks, appeal to the customers and impact on customer satisfaction. The analysis suggests that ActivoBank might not be in a position to adopt these technologies at this point in time.

Scaling In-Memory databases on multicores

Current computer systems have evolved from featuring only a single processing unit and limited RAM, in the order of kilobytes or few megabytes, to include several multicore processors, o↵ering in the order of several tens of concurrent execution contexts, and have main memory in the order of several tens to hundreds of gigabytes. This allows to keep all data of many applications in the main memory, leading to the development of inmemory databases. Compared to disk-backed databases, in-memory databases (IMDBs) are expected to provide better performance by incurring in less I/O overhead. In this dissertation, we present a scalability study of two general purpose IMDBs on multicore systems. The results show that current general purpose IMDBs do not scale on multicores, due to contention among threads running concurrent transactions. In this work, we explore di↵erent direction to overcome the scalability issues of IMDBs in multicores, while enforcing strong isolation semantics. First, we present a solution that requires no modification to either database systems or to the applications, called MacroDB. MacroDB replicates the database among several engines, using a master-slave replication scheme, where update transactions execute on the master, while read-only transactions execute on slaves. This reduces contention, allowing MacroDB to o↵er scalable performance under read-only workloads, while updateintensive workloads su↵er from performance loss, when compared to the standalone engine. Second, we delve into the database engine and identify the concurrency control mechanism used by the storage sub-component as a scalability bottleneck. We then propose a new locking scheme that allows the removal of such mechanisms from the storage sub-component. This modification o↵ers performance improvement under all workloads, when compared to the standalone engine, while scalability is limited to read-only workloads. Next we addressed the scalability limitations for update-intensive workloads, and propose the reduction of locking granularity from the table level to the attribute level. This further improved performance for intensive and moderate update workloads, at a slight cost for read-only workloads. Scalability is limited to intensive-read and read-only workloads. Finally, we investigate the impact applications have on the performance of database systems, by studying how operation order inside transactions influences the database performance. We then propose a Read before Write (RbW) interaction pattern, under which transaction perform all read operations before executing write operations. The RbW pattern allowed TPC-C to achieve scalable performance on our modified engine for all workloads. Additionally, the RbW pattern allowed our modified engine to achieve scalable performance on multicores, almost up to the total number of cores, while enforcing strong isolation.

STAR: Speech Therapy with Augmented Reality for children with autism spectrum disorders

Graphics based systems of Augmented and Alternative Communication are widely used to promote communication in people with Autism Spectrum Disorders. This study discusses an integration of Augmented Reality in communication interventions, by relating elements of Augmented and Alternative Communication and Applied Behaviour Analysis strategies. An architecture for an Augmented Reality based interactive system to assist interventions is proposed. STAR provides an Augmented Reality tool to assist interventions performed by therapists and support for parents to join in and participate in the child’s intervention. Finally we report on the usage of the Augmented Reality tool in interventions with children with Autism Spectrum Disorders.

Column-based databases: estudo exploratório no âmbito das bases de dados NoSQL

O aumento da quantidade de dados gerados que se tem verificado nos últimos anos e a que se tem vindo a dar o nome de Big Data levou a que a tecnologia relacional começasse a demonstrar algumas fragilidades no seu armazenamento e manuseamento o que levou ao aparecimento das bases de dados NoSQL. Estas estão divididas por quatro tipos distintos nomeadamente chave/valor, documentos, grafos e famílias de colunas. Este artigo é focado nas bases de dados do tipo column-based e nele serão analisados os dois sistemas deste tipo considerados mais relevantes: Cassandra e HBase.

Monitoring and analysis of queries in distributed databases

Dissertação de Mestrado em Engenharia Informática

Column-based databases: estudo exploratório no âmbito das bases de dados NoSQL

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Document-based databases: estudo comparativo no âmbito das bases de dados NoSql

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação