In direct speech the information go from people firstly to second (people firstly said
direct to people second.In reported speech the information go from
firstly to second person to third person. By then sentence is changed about gammar
Từ loại trong tiếng Anh (Parts of Speech)
.Để nói đúng tiếng Anh, trước tiên bạn cần phải hiểu rõ chức năng của từng loại từ trong câu. Và bài viết sau sẽ giúp bạn thông suốt vể điều đó nhé!
1. Danh từ (noun) Danh từ được sử dụng để gọi tên người và sự vật. Danh từ được dùng làm chủ từ (subject), túc từ (object) hoặc bổ ngữ (complement) trong câu.
Reported speech - Câu gián tiếp
.Trong tiếng Anh, có rất nhiều cách giao tiếp cũng như cách viết. Nhưng một điều chung giữa nói và viết là khi ta trình bày những câu chữ trực tiếp hay gián tiếp. Và với bài viết sau sẽ cho chúng ta hiểu rõ hơn thế nào là câu gián tiếp trong tiếng Anh nhé.
This thesis examines how artificial neural networks can benefit a large vocabulary, speaker
independent, continuous speech recognition system. Currently, most speech recognition
systems are based on hidden Markov models (HMMs), a statistical framework that supports
both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs
make a number of suboptimal modeling assumptions that limit their potential effectiveness.
Tăng cường Windows Speech Recognition bằng các Macro
Trong bài viết này, chúng ta sẽ thảo luận về Windows Speech Recognition và làm cách nào để tăng cường chức năng của nó bằng cách dùng macro. Chúng ta sẽ học cách làm cách nào tạo macro để làm các việc như: chèn những khối văn bản xác định, chạy những chương trình với tham số xác định và gửi các keystroke đến những ứng dụng bất kỳ.
Tham khảo luận văn - đề án 'luận văn:nghiên cứu ứng dụng mã nguồn mở microsoft sdk speech 5.1 để xây dựng phần mềm luyện phát âm tiếng anh', luận văn - báo cáo, thạc sĩ - tiến sĩ - cao học phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả
Aims of the study: Common errors of reported speech made by grade 11 students at Doc Binh Kieu high school, Kien Giang province to find out grade 11 students common errors in using reported speech; to suggest some solutions to help the students avoid these errors.
What are the compelling reasons for carrying out dynamic speech modeling? We provide the answer in two related aspects. First, scientific inquiry into the human speech code has been relentlessly pursued for several decades. As an essential carrier of human intelligence and knowledge, speech is the most natural form of human communication. Embedded in the speech code are linguistic (as well as para-linguistic) messages, which are conveyed through four levels of the speech chain.
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition.
This paper presents a method to develop a class of variable memory Markov models that have higher memory capacity than traditional (uniform memory) Markov models. The structure of the variable memory models is induced from a manually annotated corpus through a decision tree learning algorithm. A series of comparative experiments show the resulting models outperform uniform memory Markov models in a part-of-speech tagging task.
My first contact with speech coding was in 1993 when I was a Field Application
Engineer at Texas Instruments, Inc. Soon after joining the company I was assigned
to design a demo prototype for the digital telephone answering device project.
Initially I was in charge of hardware including circuit design and printed circuit
board layout. The core of the board consisted of a microcontroller sending
commands to a mixed signal processor, where all the signal processing tasks—
including speech coding—were performed.
In this work we address the problem of unsupervised part-of-speech induction by bringing together several strands of research into a single model. We develop a novel hidden Markov model incorporating sophisticated smoothing using a hierarchical Pitman-Yor processes prior, providing an elegant and principled means of incorporating lexical characteristics.
We present a statistical model of Japanese unknown words consisting of a set of length and spelling models classified by the character types that constitute a word. The point is quite simple: different character sets should be treated differently and the changes between character types are very important because Japanese script has both ideograms like Chinese (kanji) and phonograms like English (katakana). Both word segmentation accuracy and part of speech tagging accuracy are improved by the proposed model. ...
This paper presents an algorithm for learning the probabilities of optional phonological rules from corpora. The algorithm is based on using a speech recognition system to discover the surface pronunciations of words in spe.ech corpora; using an automatic system obviates expensive phonetic labeling by hand. We describe the details of our algorithm and show the probabilities the system has learned for ten common phonological rules which model reductions and coarticulation effects.
To understand a speaker's turn of a conversation, one needs to segment it into intonational phrases, clean up any speech repairs that might have occurred, and identify discourse markers. In this paper, we argue that these problems must be resolved together, and that they must be resolved early in the processing stream. We put forward a statistical language model that resolves these problems, does POS tagging, and can be used as the language model of a speech recognizer.