课程名称︰数位语音处理概论
课程性质︰选修
课程教师︰李琳山
开课学院：电资学院
开课系所︰电机、资工系
考试日期（年月日）︰2004.05.18
考试时限（分钟）：120
是否需发放奖励金：是
（如未明确表示，则不予发放）
试题 :
Digital Speech Processing / Midterm
May 18 2004 10:10 a.m. ~ 12:10 p.m.
● OPEN EVERYTHING
● 除专有名词可用英文外，所有文字说明一律以中文为限，未用中文者不计分。
● Total points: 120, Time allocation: 1 point / 1 minute
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1. (15) Describe the basic elements, operations and relevant research issues
of spoken dialogue systems.
2. (10)
(a) (5) What are voiced/unvoiced speech signals and their time domain
waveform characteristics?
(b) (5) What is the pitch in speech signals and how is it related to the
tones in Mandarin Chinese?
╴
3. (20) Given a HMM λ = (A, B, π), an observation sequence Ｏ = o_1 o_2 ...
o_t ... o_T and a state sequence q(上面加底线) = q_1 q_2 ... q_t ... q_T,
define
α_t(i) = prob(o_1 o_2 ... o_t, q_t = i│λ)
β_t(i) = prob(o_(t+1) o_(t+2) ... o_T│q_t = i, λ)
╴ N
(a) (5) Show that prob(Ｏ│λ) = Σ [α_t(i) β_t(i)].
i=1
╴ α_t(i) β_t(i)
(b) (5) Show that prob[q_t = i│Ｏ, λ] = ───────────
N
Σ [α_t(i) β_t(i)]
i=1
(c) (10) Formulate and describe the procedures for Viterbi Algorithm to find
the best state sequence q*(上面加底线) = q_1* q_2* ... q_t* ... q_T*.
4. (10) Explain what the segmental k-means algorithm is and how it works.
5. (10) Explain: in designing the decision tree to train tri-phone models, how
the information theory is used to split a node n into two nodes a and b.
6. (12) The perplexity of a language source S is
H(S)
PP(S) = 2 , H(S) = -Σ p(x_i) log[p(x_i)],
i
where x_i is a word in the language, Explain why PP(S) is the estimate of
the branching factor for the language assuming a "virtual vocabulary"?
7. (8) Explain what the class-based language model is and why it is useful.
8. (15) For large vocabulary continuous speech recognition, explain how the
Viterbi algorithm can be performed over a lexicon tree of phone unit HMMs
such that the knowledge from the acoustic models, lexicon nad language
model can be efficiently integrated?
9. (20) Write down anything you learned about the following subjects which
were NOT mentioned in class. Don't write down anything mentioned in the
class.
(a) (10) speech signals or acoustic modeling
(b) (10) language model analysis or smoothing